From: Applications of wearable sensors in upper extremity MSK conditions: a scoping review
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? (✓/×) |
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(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 | ×/×/✓ |