Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review

Abstract

Motion capture systems are recognized as the gold standard for joint angle calculation. However, studies using these systems are restricted to laboratory settings for technical reasons, which may lead to findings that are not representative of real-life context. Recently developed commercial and home-made inertial measurement sensors (M/IMU) are potentially good alternatives to the laboratory-based systems, and recent technology improvements required a synthesis of the current evidence. The aim of this systematic review was to determine the criterion validity and reliability of M/IMU for each body joint and for tasks of different levels of complexity. Five different databases were screened (Pubmed, Cinhal, Embase, Ergonomic abstract, and Compendex). Two evaluators performed independent selection, quality assessment (consensus-based standards for the selection of health measurement instruments [COSMIN] and quality appraisal tools), and data extraction. Forty-two studies were included. Reported validity varied according to task complexity (higher validity for simple tasks) and the joint evaluated (better validity for lower limb joints). More studies on reliability are needed to make stronger conclusions, as the number of studies addressing this psychometric property was limited. M/IMU should be considered as a valid tool to assess whole body range of motion, but further studies are needed to standardize technical procedures to obtain more accurate data.

A Practical Gait Feedback Method Based on Wearable Inertial Sensors for a Drop Foot Assistance Device

“…Results in Table III showed that the quality of angle measurement decreases while the treadmill walking speed increases. This is consistent with results in previous studies (reviewed in [41]). Skin tissue artefact (STA) is a major source of RMSE by applying additional noise to the bodyworn sensors [42].…”

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

“…For functional calibration movements 4a, 4b, and 4c, the second axis was defined as the principal acceleration axis through a PCA on accelerometer signals transformed in a lab-fixed reference frame. The 3D orientation of the pelvis and joint angles for the hips, knees, and ankles were calculated from the segment orientations based on the recommendations of the International Society of Biomechanics [6] for the different functional calibration movements. Flexion-extension were rotations around the X axis, abduction-adduction was around the Y axis, and internal-external rotations were around the Z axis.…”

“…Although they generally contain a 3D accelerometer, a 3D gyroscope, and an optional 3D magnetometer, an IMU does not measure joint angles perfectly. Joint angles obtained via signal integration typically drift over time [4,5] and their accuracy varies with the joint assessed and the movement complexity [6,7]. While an error under 5 • is generally accepted for most clinical gait applications [8], the measurement error typically ranges from 5 • to 18.8 • depending on the joint and the plane of motion [6].…”

“…Joint angles obtained via signal integration typically drift over time [4,5] and their accuracy varies with the joint assessed and the movement complexity [6,7]. While an error under 5 • is generally accepted for most clinical gait applications [8], the measurement error typically ranges from 5 • to 18.8 • depending on the joint and the plane of motion [6]. Another major challenge in IMU-based human motion analysis that the IMUs' local coordinate systems are not aligned with physiologically meaningful axes [9].…”

Upper Body Pose Estimation Using Wearable Inertial Sensors and Multiplicative Kalman Filter

“…A further way to estimate the pose of the human body consists in using Micro Electro-Mechanical Systems (MEMS), such as Magnetic, Angular Rate, and Gravity (MARG) sensors, including triaxial gyroscopes, accelerometers, and magnetometers [10], [11]. These sensors can be easily integrated with wearable devices and can be used to reconstruct the pose of the human body through specific algorithms [12], [13], [14].…”