Introduction
Materials and methods
Databases and systematic search
Selection criteria
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Adults with MSK condition or the at-risk of developing an MSK disorder or condition
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Utilizing wearable sensors
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Sensor placement or assessment of outcomes related to shoulder/upper arm, elbow/forearm/wrist, and hand/finger
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Studies published in peer-reviewed journals and conference abstracts with available full text from 2010
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Wearable sensors applied on robots
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Exoskeletons
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Brain or human–computer interfaces (BCI/HCI)
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Focusing on gait or balance
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Measurements or applications not related to MSK rehabilitation or assessment
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Inadequate details of hardware or measured outcomes of wearable sensors (insufficient details)
Data extraction and analysis results
Results
Author year | Participants (Intervention/Control) | Study design type | Aim | Sensor placement | Sensor type, model (provider), utilized number, and wearable platform [core processing unit is included in case of reporting by authors] | Measured outcome(s) [secondary outcomes are indicated with ‘secondary’] | Software for processing/data display | Home- based/ comfortability/ wireless? (✓/×) |
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(1) Duc et al. 2014 [38] | 20 subjects Intervention: 10 patients with rotator cuff tear Control:10 healthy subjects | Cross sectional case control | (1) Quantifying the muscular activation duration in the upper trapezius, medial deltoid, and biceps brachii during movement (2) Investigating the rotator cuff tear impression in both laboratory and daily life settings | Shoulders | (1) 2 IMU sensors of ADXRS and ADXL (triaxial gyroscope, and accelerometer) from Analog devices (Analog devices Inc., USA) (2) 4 EMG sensors (Biometrics® SX230, UK) with sampling frequency of 1.6 kHz | (1) Duration of humorous movements (s) (2) Duration of muscular activation (s) in medial deltoid and biceps brachii | No information | ✓/×/× |
(2) Pichonnaz et al. 2015 [45] | 62 subjects Intervention: 21 patients after rotator cuff surgery Control: 41 healthy subjects | Longitudinal case control [3, 6, and 12 months after surgery] | (1) Assessment of the underuse rate of relevance as an upper limb postsurgical function indicator (2) Applying a new metric to investigate the effect of the rotator cuff surgery on arm usage during the first year after surgery | Shoulders | (1) 2 IMU modules each containing 3 ADXRS gyroscopes and 3 ADXL accelerometers (Analog Devices, Norwood, MA, USA) | (1) Arm activity usage (%) | No information | ✓/×/× |
(3) Duc. et al. 2013 [46] | Lab phase: 11 subjects Clinical phase: 21 patients after rotator cuff surgery Control: 41 healthy subjects | Longitudinal Case control [3, 6, and 12 months after surgery] | (1) Validating a method using wearable inertial sensors for detecting humerus movements relative to the trunk (2) Providing new outcome parameters for arm movement during long-term measurement of daily activity | Shoulders | (1) 2 IMU modules each containing 3 ADXRS gyroscopes and 3 ADXL accelerometers (Analog Devices, Norwood, MA, USA) | (1) The movement frequency of arms (number of arm movements/ the number of sitting and standing hours) (2) The symmetry index of Fr in dominant and non-dominant arms (%) [Using duration of movements (h) and angular velocity of arms (°/s)] | No information | ✓/×/× |
(4) Najafi et al. 2021 [50] | 29 subjects Intervention: 16 neurogenic thoracic outlet syndrome (nTOS) patients Control: 13 healthy subjects | Cross-sectional case control | (1) Identifying digital biomarkers of upper extremity function impacted by nTOS (2) Using the biomarkers for objectively defining nTOS severity | Upper arm | (1) An IMU system (LEGSys™, BioSensics, Newton, MA, USA) attached using an adjustable strap (sampling frequency = 100 Hz) | (1) Joint angle, and angular velocity in 3 axes (yaw, pitch, roll; °, and °/s) (2) Number of performed cycles (3) Duration of abduction and adduction movements (ms) | MATLAB | ×/×/× |
(5) Van de Kleut. et al. 2021 [47] | 33 subjects indicated for reverse total shoulder arthroplasty (RTSA) | Longitudinal case series [1 month before surgery, and 3-month and 1-year after surgery] | (1) Proposing a new system to monitor and record arm motion and activity at home preoperatively and postoperatively RTSA using portable wearable sensors | Shoulders | (1) 2 IMU sensors (3-Space Data Logger, Yost Labs, Portsmouth, OH, USA, (sampling frequency = 10Hz) fitted in a tight-fitting compression shirt of Nike (Beaverton, OR, USA) | (1) Number of elevation events/h (2) Percentage of time spent within different elevation ranges of 0◦–20◦, 20◦–40◦, 40◦–60◦, 60◦–80◦, 80◦–100◦, and > 100◦ (%) (3) The percentage of occurred elevation events within the elevation ranges (%) (4) Intensity of arm activity (low, moderate, or high) [Using upper arm joint angles in 3 axes (yaw, pitch, roll; °)] | MATLAB | ✓/×/× |
(6) Kwak et al. 2019 [24] | 24 patients with rotator cuff disease | Cross-sectional case series | (1) Measuring motion smoothness–based parameters (motion quality) of the shoulder (2) Proposing new parameters to discriminate between healthy shoulders and shoulders with rotator cuff tear | Wrist | (1) 1 IMU sensor (LogonU, Seoul, Republic of Korea) attached to the wrist using a stretchable fabric | (1) The number of peaks in the sum of angular velocity (2) The peak velocity–to–mean velocity ratio (ratio of the maximum recorded angular velocity of the motion and the mean angular velocity) (3) The number of sign reversals in angular velocity (number of zero crossings) [Using angular velocity and acceleration of arms (°/s, g)] | MATLAB | ×/×/× |
(7) Larrivée et al. 2019 [25] | 38 subjects affected by rotator cuff tendinopathy | Longitudinal case series [1 week before, the same day, 2 and 4 weeks after the corticosteroid injection] | (1) Evaluation and comparison of test–retest reliability and sensitivity to change of clinical assessments of shoulder function to wrist-based inertial measures of shoulder motion (2) determining the acceptability and compliance of using wrist-based wearable sensors | Wrist of affected shoulder | Self-developed system called WIMU-GPS: (1) 1 Wireless IMUs including GPS (SiRFstarIV, 48 Channels, sampled at 1Hz), (inertial data sampling frequency = 50 Hz) embedded in a fabric wrapping around the wrist | (1) Active time; reported as a ratio with total recording time (ratio) (2) Mean activity count (AC) per minute (ratio) sorted in 3 categories of low-intensity (LIA), medium-intensity (MIA), and high-intensity activities (HIA) [Using 3 axes acceleration data (g)] | No information | ✓/✓/✓ |
(8) Burns et al. 2018 [26] | 20 Healthy subjects | Cross-sectional case series | (1) Developing and evaluating the potential for performing home shoulder physiotherapy monitoring using a commercial smartwatch and a new classification method | Wrist | (1) Apple Watch (Series 2 & 3, Apple Inc., USA) containing 6-axis acceleration and gyroscope (sampling frequency = 50 Hz) | (1) Acceleration and rotational velocity in all 3 axes (yaw, pitch, roll; g, and °/s) | PowerSense app | ✓/×/✓ |
(9) Burns et al. 2020 [27] | 42 patients with rotator cuff pathology | Longitudinal case series [monthly visits for a year after treatment] | (1) Validity assessment of a wearable sensor system for evaluating participation and technique adherence of shoulder exercise (2) Quantifying the rate of home physiotherapy adherence, and its effect on recovery (3) Designing a pilot test for an ethically conscious adherence-driven rehabilitation program specialized for each individual | Wrist | (1) A Huawei 2 smartwatch (Huawei Technologies Co Ltd, China, sampling frequency = 50 Hz) | (1) Participation adherence rate (%) over each 2-week interval of treatment (2) shoulder active range of motion in 3 axes (yaw, pitch, roll; °) | No information | ✓/×/✓ |
(10) Hurd et al. 2018 [28] | 14 subjects undergoing Reverse Shoulder Arthroplasty (RSA) | Longitudinal case series | (1) Evaluating the changes in pain, self-reported function, and limb activity and their correlation and difference after RSA using subjective and wearable sensor-measured outcomes | Wrist and upper arm | 2 Triaxial GT3XP-BTLE IMUs (ActiGraph, Pensacola, Florida, USA, sampling frequency = 100 Hz) attached with Velcro straps | (1) Mean limb activity (m/s2/epoch) (2) The activity frequency categorized in 3 levels of inactive, low, and high (%) [Using 3 axes accelerometer data (g)] | MATLAB | ✓/×/× |
(11) Ajcevic et al. 2020 [51] | 13 subjects Intervention: 6 subjects with adhesive capsulitis (frozen shoulder) Control: 7 healthy subjects | Longitudinal case control [at the beginning of study and after 3 months] | (1) Investigating the possibility of quantitative evaluation of capsulate-related deficit versus healthy controls (2) Assessment of treatment efficacy by measurement of shoulder kinematic parameters using IMUs | Shoulder (scapula and humerus) | 2 MTw wireless IMUs (Xsens Technologies, Netherlands) placed on an elastic cuff | (1) The range of motion in elevation, and abduction movements of scapula and humerus (yaw, and pitch; °) (2) Activation time of the scapula and humerus (s) | No information | ×/×/× |
(12) Chen et al. 2020 [44] | Reliability: 25 subjects [ 10 for control group and 15 with Adhesive Capsulitis Effectiveness: 15 subjects [7 for home-based exercise and 8 for motion sensor–assisted] | Longitudinal case control [at the beginning of study and after 1, 2, and 3 months] | (1) Verifying the reliability and effectiveness of a treatment model using a wearable motion sensor device to assist AC patients by performing home-based exercises to improve training compliance and the accuracy of exercises | Upper arm and Wrist | (1) 1 Motion sensor device (BoostFix wearable self-training kit, COMPAL Electronics Inc, Taipei, Taiwan) containing 6-axis microelectromechanical systems attached to body parts with straps | (1) Active ROM and passive ROM of shoulder (yaw, pitch, roll; °) (2) Exercise completion rates (%) | Doctor and patient apps developed by BoostFix | ✓/×/× |
(13) Aslani et al. 2018 [39] | 7 Subjects [6 healthy subjects and 1 subject with frozen shoulder] | Cross-sectional case series | (1) Developing and evaluating a single IMU accompanied by an EMG sensor for monitoring the 3D reachable workspace along with simultaneous measurement of deltoid muscle activity | Upper arm | (1) IMU sensor: 1 BNO055 (Adafruit, USA) attached with an adjustable band (2) EMG sensor: MyoWare Muscle Sensor | (1) Shoulder range of motion in Spherical coordinate (azimuthal angle and elevation angle) [Reference point: Top shoulder joint] (2) RMS values of EMG amplitude (V) | MATLAB | ×/×/✓ |
(14) Yin and Xu-2018 [29] | 1 Healthy subject | Preliminary study | (1) Proposing a system to recognize the movements of patients that require upper limb rehabilitation like frozen shoulder patients | (1) Upper arms, forearms, and hands | (1) 6 IMU sensors embedded in a designed stretchable sleeve (No further details) | (1) Joint elevation angles in all 3 axes (yaw, pitch, roll; °) | Self-developed game through Microsoft® XNA game engine | ✓/×/✓ |
(15) Xuedan et al. 2019 [42] | No information | Preliminary study | (1) Proposing new treatment of shoulder periarthritis using near-infrared light through stimulation of the acupuncture points on frozen shoulder | Shoulder (scapulohumeral area) | (1) High-power LEDs (800mW) with wavelength of 940nm (near infrared) mounted on flexible circuit boards were attached to a mesh vest by a nylon Velcro (voltage:1.4–1.7 V; current:700–1000 mA) | (1) Peak optical power (W) (2) peak optical density (mW/cm2) | Self-developed software | ×/×/× |
(16) Körver et al. 2014 [52] | Control: 100 healthy subjects Intervention: 15 patients with subacromial impingement syndrome | Longitudinal case control [at diagnosis and 5 years after diagnosis] | (1) Objective assessment of shoulder movements in patients with subacromial impingement syndrome at baseline and at five-year after treatment using inertial movement sensor measurements | Upper arm | (1) 1 Inertia-Link-2400-SK1 IMU sensor (MicroStrain, Inc., Williston, Vermont, USA) fixed with an adhesive patch | Upper arm accelerations and angular velocity or rate in 3 axes (yaw, pitch, roll; g, and °/s) | 3DM-GX2 Software Development Kit | ×/×/× |
(17) Lorussi et al. 2019 [43] | 10 Healthy subjects | Cross-sectional case series | (1) Development of a shoulder physiotherapy application (called ShoulPhy) for shoulder impingement syndrome patients with the aims of (a) remote monitoring of the subject’s adherence to the program (b) providing a quantitative evaluation of the therapeutic activity and functional level (c) designing individualized exercises by the therapists | IMUs: Wrists Strain: spine to shoulder (scapula) | (1) 2 IMUs (MTw; XSens, Netherlands) embedded in wearable straps; 1 textile strain sensor (optical detection of relative positions of markers is made by Smart DX 100 (BTS Bioengineering, USA) | (1) Shoulder and wrists joint angles in 3 axes (yaw, pitch, roll; °) with respect to standing position reference (2) The measurements error of software in 3 axes (yaw, pitch, roll; °) | ShoulPhy (self-developed app) | ✓/×/× |
(18) Carmona-Ortiz et al. 2020 [53] | A healthy subject and a subject with Becker muscular dystrophy | Cross-sectional case series | (1) The development of a fully portable, nonobtrusive, and wearable IMU-based motion measurement system called the ArmTracker tracking arm and torso kinematics during daily life | Upper arms and forearms | (1) 4 IMU sensors (BNO055, Bosch Sensortec GmbH, sampling frequency = 50 Hz) encapsulated in a 3D printed plastic case (2) a microcontroller (Teensy 3.6, PJRC.COM, LLC.) [All components are embedded in a Lycra shirt.] | (1) Arm and wrists joint angles and acceleration (yaw, pitch, roll; °, g) | MATLAB | ✓/×/× |
(19) Zucchi et al. 2020 [30] | 40 subjects with distal radius fractures [20 treated with Kirschner wire fixation and 20 subjects treated with volar plate fixation] | Cross-sectional case control | (1) Retrospective evaluation of wrist ROM for patients in recovery after Kirschner wire fixation and volar plate fixation surgical treatment, using an IMU thereby evaluating the presence of compensatory movements (2) Assessment of the presence of muscle fatigue, through sEMG | IMU: hand EMG: upper arm | (1) IMU: a single IMU sensor (Fisiocomputer, Rome, Italy) attached to hand with a band (2) sEMG: a multichannel Pocket Free EMG system (BTSengineering, USA, sampling frequency = 1000 Hz) | (1) Affected wrist ROM (°) and in percentages with respect to the unaffected wrist (%) through ulnar and radial deviation (yaw), flexion and extension (pitch), and pronation and supination of forearm (2) EMG amplitude (V) | Fisiocomputer, and BTSengineering software | ×/×/× |
(20) Perraudin et al. 2018 [31] | Intervention: 30 patients with osteoarthritis, rheumatoid arthritis, or psoriatic arthritis Control: 15 healthy subjects | Cross-sectional case control | (1) The feasibility of performing unsupervised, correct, and consistent sit to stand tests at home (2) Finding a model that can that demonstrate the relationship of the tests duration obtained from sensors to pain and stiffness | Wrist | 1 ActiGraph GT9X IMU sensor (ActiGraph Inc., USA, sampling frequency = 30 Hz) mounted on a wristband | (1) The 3-axis accelerometer data (g), and its spherical coordinates (norm vector, azimuth in degrees, elevation in degrees) (2) The duration of the 5 × STS tests (s) (3) Adherence to the tests (%) | Self-developed smartphone app | ✓/×/× |
(21) Kassanos et al. 2019 [32] | No information | Preliminary study | (1) proposing a new design for (a) temperature sensing and heating capabilities for localized temperature measurements (b) thermotherapy and closed-loop thermoregulation for the arthritis patients | Wrist | (1) A flexible printed circuit (FPC) with a 50 μm thick, 26 mm wide and 188 mm long, and made from polyimide substrate (wrapped around the wrist) | Temperature of element placed on wrist (°c) | No information | ×/×/× |
(22) Murad et al. 2017 [33] | 1 Healthy subject | Preliminary study | (1) Providing rehabilitation programs through music therapy for individuals with motor impairments using Motion Initiated Music Ensemble with Sensors (MIMES) | Wrist | (1) A smartwatch (no further details) | (1) 3 Axes accelerometer signals of wrist (yaw, pitch, roll; g) | No information | ✓/×/✓ |
(23) Holland et al. 2020 [34] | Control: 17 healthy subjects Intervention: 10 patients with hand arthritis | Cross sectional case control | (1) Evaluating the appropriateness of ‘‘arthritic’’ designed golf grips for patients with hand arthritis by assessment of total applied grip force and grip configuration (ROM) using 12 ‘‘arthritic’’ designed golf grips in golfers with and without hand arthritis | Force: fingers (tips of thumb, index, middle, and ring fingers) | (1) FingerTPS system containing 3 sensors (Pressure Profile Systems, Los Angeles, CA, USA, sampling frequency = 50Hz) | (1) Forces at the distal palmar aspect of the thumb, index, middle, and ring finger of each participant’s trail hand (Pounds per square inch or psi) (2) Grip configuration or ROM of the thumb and index fingers (°) | Dartfish Movement Analysis Software/ Chameleon Visualization Software | ×/×/× |
(24) Silişteanu et al. 2016 [36] | 25 subjects with the Carpal tunnel syndrome | Longitudinal case series [followed by a 30-day duration] | (1) Limiting the flexion/extension movement at the level of hand and fingers by using sensor gloves reducing the recovery time of Carpal tunnel syndrome | Hand | (1) VPL Data Glove (Sun Microsystems Inc., USA) containing 14 optical sensors embedded in a fabric glove | (Secondary) (1) The positions of fingers (no information regarding the details) | No information | ×/×/× |
(25) Connolly et al. 2018 [54] | 9 Subjects with significant (not severe) pain in their hands | Cross-sectional case series | (1) Development of a novel wireless smart glove called iSEG-Glove to facilitate objective accurate measurement of fingers joint movement | Fingers and hand | (1) iSEG-Glove system containing: (a) 16 9-axes IMU sensors; MPU-9150 (TDK InvenSense, Japan) (b) AVR32; UC3C 32 Bit Microcontroller | (1) ROM and angular velocity of finger joints (yaw, pitch, roll; °, °/s) | Self-developed GUI | ×/×/✓ |
(26) Mack and min-2019 [37] | No information | Preliminary study | (1) Developing a wireless wearable wrist positions detection system that monitors symptoms of Carpal Tunnel Syndrome using flex sensors | Wrist | (1) 2 Capacitive resistance flex sensor and potentiometer (Spectra symbol, USA) embedded in a thin fabric glove (2) Arduino Pro Mini; ATmega328 AVR microcontroller | Wrist angle (pitch, °) | MATLAB (Self-developed GUI) | ×/×/✓ |
(27) O’Quigley et al. 2014 [35] | No information | Preliminary study | (1) Proposing a glove for home-monitoring of Rheumatoid Arthritis (RA) patients through assessment of finger joints movement (2) Comparison of the proposed developed sensor glove based on piezo-resistive fabrics with a motion capture VICON Nexus system | Piezo-resistive: Fingers IMU: top of the hand | (1) 10 piezo-resistive sensor fabrics (LR and LTT of Eeonyx, USA), and 1 IMU sensor (No further details) embedded in a glove (2) Arduino Fio as the core processing unit | Finger joint angles (°) | Self-developed GUI | ✓/×/✓ |
(28) Langohr et al. 2018 [48] | 36 Subjects undergone shoulder arthroplasty Control: the contra- lateral asymptomatic joint of subjects | Cross sectional case control | (1) Determining the total daily shoulder motion of patients following TSA and RTSA (2) Comparing the mobility of the arthroplasty shoulder with the asymptomatic joint (3) Comparing the daily motion of TSA and RTSA shoulders | Upper arms, forearms, and torso | (1) 5 9-axes IMU sensors (YEI Technology, Portsmouth, OH, USA) embedded in sewn pockets of a stretchable shirt (2) An external battery pack affixed to a belt (3) a tight-fitting long-sleeved spandex shirt (Nike, Beaverton, OR, USA) | (1) ROM and shoulder joint angles (°) (2) Percentage of time spent in different angle ranges of elevation and plane of elevation axes (%) (3) Number of motions per hour in each angle range | LabVIEW (Self-developed GUI) | ✓/×/× |
(29) Haverstock et al. 2020 [49] | Control: 13 healthy subjects Intervention: 33 Subjects undergone shoulder arthroplasties | Cross sectional case control | (1) Determining the posture and cumulative elbow motion during one-day daily activities (2) Comparing elbow motions of both dominant and nondominant arms | Upper arms, forearms, and torso | (1) 5 9-axes IMU sensors (YEI Technology, Portsmouth, OH, USA) embedded in sewn pockets of a stretchable shirt (2) An external battery pack affixed to a belt (3) a tight-fitting long-sleeved spandex shirt (Nike, Beaverton, OR, USA) | (1) ROM and forearm joint angles in flexion/extension and pronation/supination postures (°) (2) Percentage of time spent in different angle ranges (%) (3) Number of elbow motions per hour in each angle range | LabVIEW (Self-developed GUI) | ✓/×/× |
(30) Lavado and Vela 2022 [40] | 5 Healthy subjects | Cross-sectional case series | (1) Design and implementation of an IMU- and EMG-based wearable device for telemonitoring the elbow flexion- extension angle and muscle activity of patients’ rehabilitation | IMU: Forearm EMG: Upper arm (biceps brachii and triceps brachii) | (1) Gravity Analog EMG sensor (OYMotion, China) (2) MPU-6050 (InvenSense Inc, USA); a 3-axis gyroscope and a 3-axis accelerometer (3) Arduino Nano as the core processing unit | (1) ROM and flexion–extension elbow joint angle (°) (2) EMG amplitude of biceps brachii and triceps brachii (V) | MATLAB (Self-developed GUI) | ×/×/✓ |
(31) Rigozzi et al. 2022 [41] | 4 Healthy tennis players | Cross-sectional case series | (1) Developing a novel microcontroller-based wearable device to measure grip strength, forearm EMG activity and vibrational transfer aiding the diagnose of elbow tendinopathy (2) Comparing the grip strength and forearm EMG activity of tennis players with different levels of experience | EMG: Forearm (extensor carpi radialis brevis and flexor carpi radialis) Accelerometer: The racket, wrist (lateral epicondyle of the distal ulnar head) and elbow (lateral epicondyle of the humerus) | A prototype called TRAM-2 attached to the handle of racket: (1) 2 EMG Muscle Sensors (MyoWare AT-04–001, Advancer Technologies, Raleigh, USA) (2) 3 accelerometer sensors (ADXL377, SparkFun, Colorado, USA) (3) a custom-built pressure sensor (Adafruit Velostat and Adafruit Copper Foil Sheet) (4) a microcontroller (Teensy 3.6, PJRC, Oregon, USA) | (1) Normalized EMG activity and grip strength to the maximum voluntary contraction (MVC) activity level (%) (2) Angular rotation of racket, wrist, and elbow (°/s) | No information | ×/×/× |
Author year | Participants (Intervention/Control) | Study design type | Aim | Sensor placement | Sensor type, model (provider), utilized number, and wearable platform [core processing unit is included in case of reporting by authors] | Measured outcome(s) [secondary outcomes are indicated with ‘secondary’] | Software for processing/data display | Comfortability/ wireless? (✓/×) |
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(1) Humadi et al. 2020 [67] | 10 Healthy workers | Cross-sectional case series | (1) Investigating the validity and repeatability of an IMU system for in-field Rapid Upper Limb Assessment (RULA) score assessment in manual handling tasks using 3D Cardan angles and 2D projection angles with respect to values of a motion-capture camera system (reference) | IMU: shoulders, forearms, and wrists Motion markers: Shoulders (scapula and humerus), and forearms | (1) 6 IMUs (MTw, Xsens Technologies, the Netherlands, sampling frequency = 60Hz) (2) Arduino microcontroller (3) A motion-capture camera system (VICON, Oxford Metrics Group, UK) with eight cameras (sampling frequency = 100Hz) | (1) Root-mean-square error (RMSE) of obtained joint angles (°) (2) RULA score (1 to 7) [Using 3 axes upper arm, lower arm and elbow joint angles (°)] | Self-developed software/ Xsens-MVN Analyze | ×/✓ |
(2) Humadi et al. 2021 [68] | 11 Healthy novice workers | Cross-sectional case series | (1) Investigating the accuracy and reliability of (a) IMU-based wearable technology and (b) a marker-less optical technology (using Kinect V2) against VICON motion-capture system (the gold standard) based on RULA risk assessment tool during different manual material handling tasks | IMUs: Upper arms, forearms, and hands VICON markers: shoulders, elbows, and wrists | (1) 6 IMUs (MTw, Xsens Technologies, the Netherlands, sampling frequency = 60 Hz) fixed with plates and attached with using double-sided medical tape and extra layers of surgical tape (2) Kinect V2 (Microsoft Corporation, USA) (3) Motion-capture camera system of VICON (Oxford Metrics Group, UK) with eight cameras (sampling frequency = 100 Hz) | (1) Median of joint angles in 3 axes (yaw, pitch, roll; °) (2) Root-mean-square error (RMSE) of obtained joint angles in IMU and Kinect methods with respect to VICON system as the gold standard (°) | Self-developed/ Xsens-MVN Analyze | ×/✓ |
(3) Lee et al. 2020 [75] | 4 Healthy observers/ subjects | Cross-sectional case series | (1) Testing the interrater reliability of the IMU-based posture-matching method, for providing applications for the IMU sensor-based motion tracking system (2) Identifying the risk of WMSD | Upper arms, wrists, and hands | (1) 3 I2M IMU Motion tracking system (NexGen Ergonomics Inc, Quebec, Canada, sampling frequency = 128 Hz) | (1) 3 Axes elbow, wrist, forearm, and upper arm joint angles (yaw, pitch, roll; °) | TK Motion Manager/ Human Motion Analyzer | ×/× |
(4) Akanmu, and Olyawela-2020 [80] | 1 Healthy carpenter | Preliminary study | (1) Examining ergonomic exposures of carpentry subtasks that might lead to musculoskeletal injuries (2) Evaluation of preventive and protective interventions impact on the musculoskeletal injuries | Upper arm, and forearm | (1) 2 smartphones (Sampling frequency = 10Hz) attached with straps and bands | (1) Joint angles (yaw, pitch, roll; °) (2) Duration of carpentry subtasks (s) | No information | ×/✓ |
(5) Ohlendorf et al. 2020 [55] | 20 Dentist teams and a student control group | Cross- sectional case control | (1) Contributing information on the prevalence of MSD in dental professionals using online questionnaires (2) Ergonomic risk assessment of treatment procedures based on RULA, focusing on treatment concepts of general dentistry, endodontology, oral surgery and orthodontics | Shoulders (scapula and humerus), and forearms | (1) 8 IMU sensors embedded in a Lycra body suite (MVN Link, XSens, Enschede, Netherlands, sampling frequency = 240Hz) | (Secondary) (1) A risk score for every recorded frame using the RULA (a mean score and SD) for wrist and arm (2) The percentage of spent time in the respective risk scores and at high-risk postures (%) | MATLAB/ Xsens-MVN Analyze | ×/✓ |
(6) Blume et al. 2021 [56] | 15 Teams consisting of a dental student a dental assistant trainee | Cross- sectional case series | (1) Investigating the ergonomic risk of dental students based on RULA score using inertial sensors during performing standardized dental activities | Upper arms (scapula and humerus), forearms, and wrists | (1) 8 IMU sensors embedded in a Lycra body suite (MVN Link, XSens, Enschede, Netherlands, sampling frequency = 240Hz) | (1) Modified RULA score (1 to 7) and relative time spent at each RULA score [Using accelerometer, angular velocity, and joint angle values in 3 axes (yaw, pitch, roll; °, °/s, g)] | Xsens-MVN Analyze | ×/✓ |
(7) Maurer-Grubinger et al. 2021 [57] | 15 Healthy oral and maxillofacial surgeons | Cross- sectional case series | (1) Developing a script that allows quantification of RULA ergonomic risk in dentistry using IMU measured data of joint angles and positions of body segments (2) Proposing various modified RULA outcomes | Upper arms (scapula and humerus), forearms, and wrists | (1) 8 IMU sensors embedded in a Lycra body suite (MVN Link, XSens, Enschede, Netherlands, sampling frequency = 240Hz) | (1) Modified RULA score (1 to 7) and relative time spent at each RULA score [Using accelerometer, angular velocity, and joint angle values in 3 axes (yaw, pitch, roll; °, °/s, g)] | Xsens-MVN Analyze | ×/✓ |
(8) Schall et al. 2021 [60] | 35 Subjects [18 manufacturing cyclic workers and 17 non-cyclic workers] | Randomized clinical trial | (1) Comparing mean levels of full-shift exposure summary metrics based on both posture and movement speed between manufacturing cyclic and non-cyclic workers (2) Exploring the patterns of between- and within-worker exposure variance and between-minute (within-shift) exposure level and variation within each group | Upper arms and dominant wrist | (1) 3 ActiGraph GT9X Link IMUs (ActiGraph, USA, sampling frequency = 100 Hz) fixed with elastic hook-and-loop fastener straps using hypoallergenic cohesive bandages | (1) Upper arms posture or angles (°), and relative time spent at neutral and extreme states (%) (2) Angular movement speed (°/s) and relative time spent at low-speed and high-speed states (%) (3) Relative time spent at 3 states of low speed, neutral, and low speed-neutral of rest/recovery exposure (%) | ActiGraph Software | ×/✓ |
(9) Merino et al. 2018 [63] | 3 Workers with musculoskeletal complaints | Cross-sectional case series | (1) Evaluating the risk of musculoskeletal injuries in banana harvesting task using EMG, IMU sensors and subjective outcomes | IMU: upper arms, forearms, and wrists EMG: upper arms | (1) IMU: 6 Xsens MVN Biomech™ sensors (Enschede, the Netherlands, Sampling frequency = 120 Hz) mounted on and attached with Velcro straps (2) EMG: Miotec 4-channel Miotool 400 (sampling frequency = 2 kHz) (3) Ag/AgCl surface electrodes of Meditrace®) in a bipolar configuration (1 cm in diameter and an inter-electrode distance of 2 cm) | (1) Mean joint angles (yaw, pitch, roll; °) and time taken to remove the bunches from the stalk (s) (2) Maximum voluntary isometric contraction (μV), peak muscle use (RMS and percentage) and mean value of signal and median frequency in each muscle (μV and Hz) | Excel/ Xsens MVN Studio Pro/ Miograph | ×/✓ |
(10) Vignais et al. 2017 [76] | 5 Healthy workers | Cross-sectional case series | (1) Performing a real-time ergonomic analysis on workers dealing with material handling tasks using IMUs and electro goniometers based on combining RULA computation and a subtask video analysis | (1) IMU: upper arms and forearms (2) Electro goniometer: wrists | (1) 4 CAPTIV Motion IMUs (TEA, Nancy, France) fixed with adjustable strap (sampling frequency = 64 Hz) (2) 2 bi-axial electro goniometers (Biometrics Ltd.,UK) attached by medical tape and straps (sampling frequency = 32 Hz) | (1) RULA score for each joint and an overall score (1 to 7), and percentage of time spent of each joint at risky levels (%) [Using 3 axes shoulder, elbow, and wrist joint angles (yaw, pitch, roll; °)] | CAPTIV software | ×/✓ |
(11) Vignais et al. 2013 [79] | 12 Healthy workers | Randomized clinical trial | (1) Performing a real-time ergonomic analysis of manual tasks in an industrial environment dealing with manual handling tasks using IMUs and electro goniometers based on RULA computation for body overall and local parts (2) Assessment of an auditory feedback to prevent development of MSK disorders in workers | Similar to previous study | (1) 4 wireless Colibri IMUs (Trivisio GmbH, Trier, Germany, sampling frequency = 100 Hz) wrapped around limbs with adhesive bands (2) Goniometer type is similar to previous study | (1) RULA score for each joint and an overall score (1 to 7), and percentage of time spent of each joint at risky levels (%) [Using 3 axes shoulder, elbow, and wrist joint angles (yaw, pitch, roll; °)] | No information | ✓/✓ |
(12) Zhang et al. 2022 [61] | 30 Manufacturing workers (2 cyclic/ non-cyclic groups of 15 workers) | Cross-sectional case control | (1) Quantification of self-reported daily discomfort, distraction and burden caused by putting on wearable inertial sensors in manufacturing workers (2) Evaluating contribution level of different personal and work characteristics on the discomfort, distraction, and burden ratings | Upper arms, and dominant wrist | (1) 3 IMU sensors of ActiGraph GT9X Links (ActiGraph, USA) fixated with elastic hook and loop fastener straps | (1) Upper arm elevation angle (°), and the magnitude of the elevation speeds (°/s) | ActiLife | ✓/× |
(13) Seidel et al. 2021 [70] | 500 Workers | Cross-sectional case series | (1) Providing direct measurements for assessment of workloads of the hand or elbow in work-field based on the Threshold Limit Value (TLV) for Hand Activity Level (HAL) (2) Finding associations between measured TLV for HAL and disorders or complaints | IMU: shoulders, forearms, elbows, and wrists EMG: Forearms | (1) CUELA multi-sensor system (IFA, Germany) containing potentiometers and IMUs (Analog Devices ADXL 103/203 3D accelerometers and muRata ENC-03R gyroscopes and goniometers), all embedded in a wearable body-shaped cloth (2) A 4-channel surface EMG module (BioMed, Germany) | (1) Mean power frequency of the power spectra of angular data (Hz) [Using 3 axes angular values of measured joints (yaw, pitch, roll; °)] (2) Mean angular velocity (°/s) (3) Kinematic micro-pauses (%) (4) HAL exposure categories (Low/Medium/High) [Using RMS values of EMG signal (V)] | CUELA designed software | ×/× |
(14) Poitras et al. 2020 [69] | 16 Healthy subjects | Cross-sectional case series | (1) Assessment of the concurrent validity of IMU units of MVN, Xsens in comparison to VICON motion capture system during simple tasks and complex lifting tasks (2) Establishment of the discriminative validity of a wireless EMG system for the evaluation of muscle activity | IMU and markers: shoulders (scapula humerus), and wrists EMG: shoulders | (1) 6 IMU sensors of MTw (MVN, Xsens Technologies, Enschede, Netherlands) fixed with hook and loop straps around arms (2) 9 Vicon MX cameras (Vicon Motion Systems Limited, Oxford, UK) (3) EMG sensors of Trigno Wireless EMG system (Delsys, Boston, MA, USA) | (1) Shoulder ROM and RMSE value with respect to VICON measurement (°) (2) RMS EMG (V, and (% of MVC) | Nexus/MVN studio/ MATLAB | ×/✓ |
(15) Bassani et al. 2021 [71] | 1 Healthy subject | Preliminary study | (1) Proposing a wearable monitoring system for sEMG and coherent motion data aimed at real-time tracking of workers’ activity for the analysis and prevention of WMSK disorders | IMU: upper arm, forearm, and hand EMG: forearm | (1) 3 MPU-9250 IMUs (Invensense Inc., USA), enabling 9-axes inertial sensing in addition to a thermal sensor to ease the compensation of the gyro values all embedded in a package and wrapped around the limbs with a stretchable strap (sampling frequency = 100 Hz) (2) 8 sEMG electrodes and acquisition systems (proposed system and g.®USBAmp of Guger Technologies) (3) STM32F407VG ARM Cortex-M4 CPU (STMicroelectronics) | (1) Signal to noise ratio of EMG signals (SNR) [Using RMS EMG signal values (mV)] (2) Acceleration, angular velocity, and angle of joints in all 3 axes (yaw, pitch, roll; g, °/s, and °) | MATLAB | ×/✓ |
(16) Lee et al. 2019 [81] | 3 Healthy subjects | Cross-sectional case series | (1) Assessment of the reliability and validity of a novel posture matching method in construction activities using IMU measurements of joint angles | Dominant upper arm wrist, and hand | (1) 3 IMU sensors (I2M IMU system; NexGen Ergonomics Inc., Canada) fixed with stretchable bands and straps through a designed platform | (1) Upper arm joint angles and RMSE values in all 3 axes (yaw, pitch, roll; °) | TK Motion Manager software/ HM Analyzer | ×/× |
(17) Peppoloni et al. 2016 [72] | 10 Healthy supermarket cashiers | Cross-sectional case series | (1) Proposing a novel wearable wireless system capable of assessing the muscular efforts using sEMG and postures of the human upper limb using IMU sensor for WMSK disorders diagnosis | IMU: upper arm, forearm, and hand, EMG: forearm | (1) 3 IMU sensors embedded inside elastic band (no further information) (2) 8-Channel sEMG (no further information) | (1) Shoulder, elbow and wrist extension/flexion, and ulnar deviation (yaw, pitch, roll; °) (2) RMS values of EMG signal (mV) and power spectral density (W/Hz) (3) Strain inde and RULA score (1 to 7) | Self-developed GUI by MATLAB | ×/✓ |
(18) Battini et al. 2014 [82] | No information | Preliminary study | (1) Introducing an innovative full-body real-time ergonomics assessment system for manual material handling in warehouse environments, using inertial sensors | Shoulders (scapula and humerus), forearms, and hands | (1) IGS-180i (Animazoo, UK) containing 8 IMU sensors for upper body section and mounted on a light full-body suit (sampling frequency = 500 Hz) | (1) Ergonomic evaluation scores such as RULA, and Lifting Index [Using shoulders (scapula), upper arms, forearms, and hands joint angles and postures with 6-DoF (yaw, pitch, roll; °) | Self-developed software | ×/✓ |
(19) Slade et al. 2021 [83] | 5 Healthy subjects | Cross-sectional case series | (1) Presenting the OpenSenseRT, an open-source and wearable system providing upper and lower extremity kinematics in real time using IMUs (2) Assessment the accuracy of the proposed system by comparing it to an optical motion capture as the gold standard seeking to achieve an RMSE of 5° or less for 3D joint angles | IMU and markers: Upper arms, forearms, and hands | (1) 6 IMU (ISM330DHCX breakout boards, Adafruit Industries Inc., USA) mounted on stretchable straps (2) A Raspberry Pi 4b + (Raspberry Pi Foundation) as the microcontroller (3) Optical motion capture system (Optitrack) | (1) Shoulder joint angles and RMSE during flexion, adduction, and rotation (yaw, pitch, roll; °) (2) Elbow joint angle and RMSE during flexion (pitch, °) (3) Wrist joint angle and RMSE during flexion (pitch, °) | Self-developed software by OpenSense tools | ×/× |
(20) Yang et al. 2020 [58] | 116 Surgery cases ( by 53 surgeons) | Cross-sectional case series | (1) Identifying risk factors and assessment of intraoperative physical stressors by subjective (questionnaires) and objective outcomes (IMU measurements) | Upper arms | (1) 2 IMU (APDM Inc, Portland, USA) [model has not been specified] (Sampling frequency = 128 Hz) | (1) Mean deviation angle of upper arms (°) (2) Percentage of spent time in the demanding posture during the surgical time (%) | MATLAB | ×/× |
(21) Hallbeck et al. 2020 [59] | 4 Healthy breast surgeons | Cross-sectional case series | (1) Comparing the ergonomics for the beast surgeons between skin-sparing mastectomy (SSM) and nipple-sparing mastectomy (NSM) using subjective and objective measures | Upper arms | (1) 2 IMU (APDM Inc, Portland, USA) [model has not been specified] (Sampling frequency = 128 Hz) | (Secondary) (1) Orientation (angles) of upper arms using mean angle (yaw, pitch, roll; °) (2) Spent time in each RULA level for postures during surgery (%) | MATLAB | ×/× |
(22) Nath et al. 2018 [84] | 2 Healthy workers | Cross-sectional case series | (1) Proposing a new methodology for evaluation of the ergonomic risk levels in warehouse operations caused by overexertion using body motion data (2) Investigating the appropriate data acquisition and processing settings through a leave-one-subject-out cross-validation framework | Upper arm | (1) 1 Smartphone (Google Nexus 5X or Google Nexus 6) strapped around the limb | (1) Acceleration (g), linear acceleration), and angular velocity in all 3 axes (yaw, pitch, roll; g, m/s2, and °/s) (2) Activity duration (s) and frequency (%) | MATLAB | ×/✓ |
(23) Jahanbanifar and Akhavian-2018 [77] | 1 Healthy construction worker | Preliminary study | (1) Developing a framework for quantification of human force as a risk indicator associated with WMSK disorders in construction workers using wearable sensors | Upper arm | (1) 1 Smartphone (sampling frequency = 35 Hz) fixated with a sports armband (2) SIMULATOR II; Functional Upper Extremity Rehabilitation device (BTEtechnologies inc., USA) | (1) F as the net force exerted (N or kg.m/s2) (2) P as the power (W or kg.m2/s3) (3) d as the displacement of the subject’s arm (m) [Using acceleration, angular velocity and posture values in all 3 axes (yaw, pitch, roll; g, °/s, and °)] (4) t as the duration of the experiments (s) | Sensor Log smartphone application/ Self-developed Python software | ×/✓ |
(24) Cerqueira et al. 2020 [74] | 5 Healthy subjects | Cross-sectional case series | (1) The design and development of an innovative smart garment providing (a) real-time ergonomic risk assessment, (b) objective data measurements to ergonomists, (c) posture awareness to operators through haptic feedback | Upper arms | (1) 2 IMUs; 9250 (Invensense Inc., USA) attached to a skinny fitted shirt (sampling frequency = 100 Hz) (2) 2 haptic motors; ERM (Precision MicrodrivesTM, London) operating at 80 Hz and 250 Hz (3) the STM32F4 ARM microcontroller | (1) Upper arm posture, and RMSE in pitch and roll axes (°) (2) Time spent percentage during each risk state or posture state (%) | MATLAB | ×/✓ |
(25) Singh et al. 2017 [85] | 4 Healthy surgeons (gynecologists) | Randomized crossover study | (1) Comparing the effect of different chairs on WMSD for surgeons during vaginal procedures based on subjective and objective outcomes (2) Assessment of the WMSD risk in gynecologists | Upper arms | (1) 2 IMU sensors of OPAL(12M SXT version APDM, Inc, Portland, USA) wrapped around limbs with adhesive bands | (Secondary) (1) The percentage of time spent in each RULA score for each body part (%) [Using shoulder elevation angle (°)] | No information | ×/✓ |
(26) Lind et al. 2020 [62] | 16 Manufacturing plant workers | Cross-sectional case series | (1) Proposing the developed haptic feedback module of the Smart Workwear System platform (2) Evaluating its user experience and preventive application for reducing biomechanical loads in light repetitive manual tasks | IMU: upper arms Haptic vibrator: 5cm lower than IMUs | The Smart Workwear System haptic feedback module embedded in a workwear shirt containing: (1) 2 IMUs (LPMS-B2 IMU, LP Research, Tokyo, Japan, sampling rate = 25Hz) embedded on stretchy workwear shirt (2) A vibration actuation unit (Precision Microdrives Limited, London, UK, feedback level = 10Hz) (3) STM32 microcontroller | (1) Angles of upper arm elevation (°) (2) Proportion of time-spent at upper-arm elevations ≥ 30◦, ≥ 45◦ and ≥ 60◦ (%) | ErgoRiskLogger smartphone application | ✓/✓ |
(27) Granzow et al. 2017 [64] | 14 Reforestation hand planters | Cross-sectional case series | (1) Characterizing the trunk and upper arm postures, movement velocities, and neck/shoulder muscle activation patterns in hand planters during full-shift work (2) Comparing the results with previous findings to understand the exposures to physical risk factors of hand planters | Shoulder | (1) IMUs: 1 ActiGraph GT9X (Actigraph, USA, sampling frequency = 100 Hz) attached with elastic neoprene straps (2) EMG electrodes (model SX230, Biometrics Ltd, Gwent, UK) | (1) Shoulder muscle forces as a percentage of the RMS EMG amplitudes observed for the submaximal reference contractions (%) (2) Upper arm flexion/extension angles (°) and angular velocity (°/s) (3) The ratio of time spent at three postures of neutral, rest and extreme, and at two velocities of low and high (%) | Self-developed GUI by LabView | ×/× |
(28) Khalil et al. 2021 [65] | 34 Baseball pitchers | Cross-sectional case series | (1) Determining the relation between medial elbow torque measured by wearable sensor IMU, and adaptations of the medial elbow structures obtained by dynamic ultrasound imaging in asymptomatic collegiate pitchers | Elbow | (1) 1 Motus Global mobile IMU sensor (Motus Global Inc., USA) embedded in a pocket of a wearable sensor baseball sleeve (Motus Global, Rockville Centre, NY) | (1) Medial elbow torque (N.m) (2) Arm rotation (maximum angle of the forearm; °), (3) Arm slot (angle of the forearm in relation to the ground at ball release; °) (4) Arm speed (maximum rotational velocity of the forearm; rotations/minute) | Motus Global smartphone application (motusTHROW) | ×/✓ |
(29) Villalobos and Maccowlry 2021 [86] | 20 Meat cutters | Cross-sectional case series | (1) Presenting an application of IMUs to perform task classification and measure work-related musculoskeletal disorders risks in meat cutters, using artificial intelligence and machine learning techniques (2) Validation of the proposed application | Wrist | (1) A Wit Motion BWT901CL Bluetooth 2.0 IMU sensor (Wit intelligence, China) attached with a Velcro strap | (1) Angle, angular velocity, and acceleration of wrist/hands (yaw, pitch, roll; °, °/s, g) (2) Wrist/hands RULA score (1 to 4) and the spent time in a risky posture based on RULA score (s) | Self-developed GUI | ×/✓ |
(30) Forsman et al. 2021 [78] | 6 Healthy subjects | Cross-sectional case series | (1) Developing and testing the validity of a method for workplace wrist velocity measurements | Wrist and hand | (1) 2 IMUs (Movesense.com, Suunto, Vantaa, Finland, sampling frequency = 50 Hz) attached with double-sided tape or an armband | (1) Wrist angular velocity (yaw, pitch, roll; °/s) | Self-developed smartphones app | ×/✓ |
(31) Rodríguez-Vega et al. 2022 [73] | 1 Healthy subject | Preliminary study | (1) Development of measurement and classification of hand movements at work, using a hand-motion capture system | Hand and fingers | (1) 6 IMU sensors (2) 6 Force-resistive sensors (No further details) | (1) Triaxial acceleration, angular velocity, and magnetic field (yaw, pitch, roll; m/s2, rad/s, and µT) (2) Exerted force each fingertip (μV) | MATLAB | ×/× |
Author year | Participants | Study design type | Aim | Sensor placement | Sensor type, model (provider), utilized number, and wearable platform [core processing unit is included in case of reporting by authors] | Measured outcome(s) [secondary outcomes are indicated with ‘secondary’] | Software for processing/data display | Home- based/ comfortability/ wireless? (✓/×) |
---|---|---|---|---|---|---|---|---|
(1) Hong et al. 2021 [87] | Subjects at risk of developing MSK disorders | Preliminary study | (1) Proposing a kirigami-structured highly anisotropic piezoelectric network composite (HAPNC) sensor for monitoring multiple information of joint motions, and sending an alarm to prevent MSK disorders | Shoulders | Multilayer HAPNC sensor: a cross-shaped polyethylene terephthalate (PET) substrate, with a square top and bottom layer with two electrodes and one piezoelectric composite | (1) Bending angle (°) (2) Bending radius (mm) (3) Voltage recorded from sensors movement (mV) | Self-developed software/ COMSOL Multiphysics | ×/×/× |
(2) Jang et al. 2020 [99] | 28 Healthy subjects | Cross-sectional case series | (1) Proposing a novel method for monitoring bad postures, including the forward head posture, rounded shoulder, and elevated shoulder | Upper arms | 2 IMU sensors of EBIMU24GV3 (E2BOX. China), embedded in a self-designed wearable fixture tool (made by a 3D printer) that can be wrapped around the shoulder (sampling frequency = 10 Hz) | The shoulder symmetry angl represented by percentage (%) [Using elevation angle of shoulders (°)] | No information | ×/×/× |
(3) Matiur Rahman et al. 2021 [97] | 10 Healthy subjects | Cross-sectional case series | (1) Identifying and discriminating subject-specific EMG signal patterns between five elbow joint angles (0°, 30°, 60°, 90° and 120°) during MVCs of sEMG | Upper arms | (1) A wearable 3 channel-based EMG system of SH-SHIM-KIT-004 (SHIMMER™, Ireland) with 1-kHz sampling rate (2) Wet Ag/AgCl surface electrodes in bipolar setting and diameter of 4 mm | (1) sEMG signal (V) | MATLAB | ×/×/× |
(4) Zabat et al. 2015 [88] | 1 Healthy athlete | Preliminary study | (1) Proposing an embedded system that measures joint angles in 3 axes using gravitational acceleration and magnetic field sensors measurements | Upper arm | (1) 1 LSM303DLHC sensor (STMicroelectronics Inc., Italy) containing a 3-axis digital accelerometer and magnetometer embedded in a prototype box package (2) PIC18f2550 microcontroller | Flexion/extension, abduction/adduction and internal/external rotations of the upper limb joint angles (yaw, pitch, and roll; °) | Self-developed GUI with Microsoft Visual C# (.NET Framework) | ×/×/× |
(5) Romero avilla et al. 2020 [96] | 10 Healthy subjects | Cross-sectional case series | (1) Demonstrating the proof-of-principle of an innovative sEMG sensor system, independently used by patients for detecting their muscular activation | Upper arm and forearm (arranged circularly) | (1) 8 EMG modules each containing two bipolar sEMG leads equipped with seven dry cap-electrodes; embedded in a rotation-symmetrical flexible armband (2) ARM microcontroller (EFM32WG 380 F 256, Silicon Labs) | (1) RMS values EMG signal (mV) | No information | ✓/×/× |
(6) Jurioli et al. 2020 [98] | 16 Healthy subjects | Cross-sectional case series | (1) Introducing a low-cost wearable device integrated to VR environments aiming to provide a better-quality rehabilitation process for most patients with motor disabilities | Forearm | (1) 1 IMU sensor of MPU-6050 (InvenSense Inc, USA) (2) Arduino Nano microcontroller (3) All instruments encapsulated in a 3D printed box made using ABS (Acrylonitrile Butadiene Styrene) plastic and wrapped around the forearm with Velcro tapes | (1) Wrist positions in relation to the elbow (yaw, and roll, °) (2) Time spent for completing the puzzle game (s) | Self-developed smartphone application | ×/✓/✓ |
(7) Elshafei and Sheihab 2021 [89] | 20 Healthy gym-goers | Cross-sectional case series | (1) Adopting a wearable approach for detecting biceps muscle fatigue during a bicep concentration curl exercise using an IMU wearable sensor | Wrist | 1 IMU (Apple Watch Series 4, Apple Inc., USA) with sampling frequency of 50 Hz | (1) X-axis and Z-axis angular velocity and posture (°/s, °), Y-axis of accelerometer (g) (2) Total exerted force of hand (N) [using product of used dumbbells mass and acceleration] | No information | ×/×/✓ |
(8) Karunarathne and Pathirana 2014 [100] | 4 Healthy subjects | Cross-sectional case series | (1) Investigating the applicability of some typical solutions of Wahba’s Problem and ordinary filtering mechanism with IMU sensor measurements for obtaining precise kinematics of human | Elbow and wrist of left arm | 2 BioKin IMU sensors (BioKin Inc., Australia) | (1) The Root Mean Square Error (RMSE) of measured angles in both methods of IMUs and VICON motion capture (°) [Using wrist and elbow joint angles, accelerations, and velocities (yaw, pitch, roll; °, °/s, g)] | No information | ×/×/✓ |
(9) Young et al. 2021 [101] | 10 Healthy subjects | Cross-sectional case series | (1) Quantifying the accuracy and evaluating the validity of a novel proposed design to assess wrist ROM with respect to optical motion capture system as a gold standard through calculating their correlation coefficient | Wrist | (1) The set (3D printed in polylactic acid); contains 2 rotary position sensor of Bourns Model 3382 (Bourns, Riverside, CA, USA) embedded in the wrist strap (2) Teensy 3.5 microcontroller (PJRC, Sherwood, OR) (3) VICON motion capture system using 5 T160 and 4 Bonita cameras (Vicon Motion Systems Ltd., UK) | (1) Wrist flexion/extension and radial/ulnar deviation angles (pitch, yaw; °) | Python | ×/×/× |
(10) Hochman et al. 2020 [94] | 7 Healthy subjects | Cross-sectional case series | (1) Proposing a novel method of measuring the joint acoustic emission of wrist using acoustic emission sensing method | Accelerometers: wrist IMU: hand | (1) 4 Uniaxial accelerometers of 3225F7 (Dytran Instruments Inc., USA) used as contact microphones (2) USB-4432 data acquisition (National Instruments, USA) recording vibrations at a sampling rate of 50 kHz (3)1 BNO-055 IMU package (Ardafruit Industries Inc., USA; Sampling frequency = 100 Hz) embedded in a silicone gel grip | (1) The signal-to-noise ratio (db) of recorded signal of microphones (uniaxial-accelerometer) (2) Wrist angle and acceleration in 3 axes (yaw, pitch, roll; °) | MATLAB | ×/×/× |
(11) Saito et al. 2017 [90] | No information | Preliminary study | (1) Introducing a novel strain sensor using pyrolytic graphite sheet (PGS), a low-cost, simple, and flexible material, for the application of wearable devices for monitoring human activity | Elbow and middle finger | (1) Small and thin films are cut from 17-µm-thick PGSs, and then attached to a flexible plastic substrate (2) Silver conducting paste as electrodes are wired on the films for electrical measurements, and the adhesives cover the silver electrodes for mechanical failure prevention | (1) Flexion/extension motion of elbow and fingers through mapping the resistance of the strain sensor (Ω) | No inforamtion | ×/×/× |
(12) Xie et al. 2020 [95] | No information | Preliminary study | (1) Proposing a novel method of monitoring biomechanical motion of joints (or eye blinking) based on electromagnetic sensing techniques | Index finger and wrist | (1) A self-developed high-speed Electromagnetic testing instrument based on Field Programmable Gate Array (FPGA), performing digital demodulation at 100 k/second and features an Ethernet communication (2) Electromagnetic coils (the excitation coil driven by an alternating current ∼48 mA rms) | (1) Finger and wrist bending level and frequency through EM impedance (Ω) | No information | ×/×/× |
(13) Smondrk et al. 2021 [91] | 1 Healthy subject | Preliminary study | (1) Designing and realizing a device for measurement of finger flexion, and extension and forearm motion (2) Assessment of the device reliability | Bend sensor: fingers IMU: forearm | (1) 5 Flexible bend sensors (Flexpoint Sensor Systems, USA) embedded inside the instrumented glove (2) A 9-axis IMU, LSM9DSO (ST Microelectronics, Switzerland) (3) A microcontroller unit of ATmega328P (Microchip Technology, USA) | (1) Fingers joint angles (pitch, °) | No information | ×/×/✓ |
(14) Zheng et al. 2016 [92] | 5 Healthy subjects | Cross-sectional case series | (1) Developing a sensor glove (called FuncAssess) and evaluation of its validity and reliability (2) Proposing a method for visualizing and quantifying the abnormality of the inter-joint coordination | Fingers | (1) Glove fabric made from polyamide stretchable fabric including sleeves for insertion of force sensors (2) 10 Bend sensors of Flexpoint Sensor Systems (Draper, USA, sampling frequency = 50 Hz) (3) 5 Force sensors of Flexiforce (Tekscan, Inc., USA) (4) MSP430 microcontroller (Texas Instruments, Inc., Dallas, TX) | (1) Finger joints bending angle (°) (2) Finger joints load (gram) | MATLAB | ×/×/× |
(15) Moreira et al. 2014 [102] | 1 Healthy subject | Preliminary study | (1) Proposing a glove aiming to (a) track hand and fingers, while minimizing drift and offset errors (b) avoid the need for cumbersome calibration procedures and (c) evaluate its reliability and validation | Hand and fingers | (1) Glove fabric is made from polyamide stretchable fabric (2) 11 9-DOF IMUs including a 3-axis gyroscope sensor of L3GD20, STMicroelectronics Inc., Italy) and 3-axis accelerometer and magnetometer sensor of LSM303DLHC (STMicroelectronics Inc., Italy), all stitched to the fabric with four fixation points (2) STM32F4 ARM processor | (1) Fingers and hand joint angles (yaw, pitch, and roll; °) | Python | ×/×/✓ |
(16) Hazman et al. 2020 [103] | 10 Healthy subjects | Cross-sectional case series | (1) Developing a glove for finger joint measurement for collecting ROM values of distal interphalangeal (DIP), proximal interphalangeal (PIP) and metacarpophalangeal (MCP) joints of an index finger | Index finger | The proposed glove: (1) Made from cloth material (1) 3 6-axis IMUs, (2) 2 2.2-inch bend sensors, (3) Arduino Nano (AVR microcontroller- ATMega328) | (1) ROM of the MCP, DIP, PIP joints in angles (yaw, pitch, and roll; °) (2) Percentage of error between both methods (%) | A self-developed GUI on MATLAB | ×/×/× |
(17) Oigawa et al. 2021 [93] | 2 Healthy subjects | Cross-sectional case series | (1) Investigating a novel method for evaluating hand movement function through fingertip data during a 10-s grip and release acquired by wearable contact-force and accelerometer sensors | Fingers | (1) 2 Contact-force sensors of HapLog (Kato Tech Co., Ltd., Kyoto, Japan) each containing (a) a triaxial accelerometer and a strain sensor mounted on the cover sensor (b) a bangle-type connector (c) a calibration unit | (1) 3 Axes, and absolute acceleration (yaw, pitch, roll; g) (2) Contact force (N) | HapLog software | ×/×/×/ |
(18) Rovini et al. 2020 [104] | 20 Healthy subjects | Cross-sectional case series | (1) Proposing an innovative, ring-shaped wearable system, called SensRing, that provides inertial data of fingers during the movement (2) Performing a preliminary technical validation to compare the measured data of the SensRing to a motion capture system of Vicon (as the gold standard) on two finger tapping exercises | Fingers | (1) SensRing: a ring-shaped wearable sensor, including (a) a LSM9DS1 IMU sensor (STMicroelectronics, Italy, sampling frequency = 50 Hz) (b) STM32-F103 ARM microcontroller (STMicroelectronics, Italy) [The processing board and device are fixed to the wrist with an elastic band] (2) The Vicon system including 8 cameras (sampling frequency = 100Hz) | (1) Triaxial accelerations and angular velocities (yaw, pitch, roll; g, °/s) (2) Number of repetitions, frequency of the movement (Hz), and range of index finger movement (°) | No information | ×/×/✓ |