Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications

Use of Technologies for the Acquisition and Processing Strategies for Motion Data Analysis

This review provides an in-depth examination of the technologies and methods used for the acquisition and processing of kinetic and kinematic variables in human motion analysis. This review analyzes the capabilities and limitations of motion-capture cameras (MCCs), inertial measurement units (IMUs), force platforms, and other prototype technologies. The role of advanced processing techniques, including filtering and transformation methods, and the increasing integration of artificial intelligence (AI) and machine learning (ML) for data classification is also discussed. These advancements enhance the precision and efficiency of biomechanical analyses, paving the way for more accurate assessments of human movement patterns. The review concludes by providing guidelines for the effective application of these technologies in both clinical and research settings, emphasizing the need for comprehensive validation to ensure reliability. This comprehensive overview serves as a valuable resource for researchers and professionals in the field of biomechanics, guiding the selection and application of appropriate technologies and methodologies for human movement analysis.

Quantifying shoulder motion in the free-living environment using wearable inertial measurement units: Challenges and recommendations

Understanding function and dysfunction of the shoulder may be best addressed by capturing the motion of the shoulder in the unstructured, free-living environment where the magnitudes and frequencies of required daily motion can be quantified. Miniaturized wearable inertial measurement units (IMUs) enable measurement of shoulder motion in the free-living environment; however, there are challenges in using IMU-based data to estimate traditionally used measures of shoulder motion from lab-based motion capture. There are limited options for IMU placement/fixation that minimize soft tissue effects and there are significant challenges in developing the algorithms that can accurately estimate shoulder joint angles from IMU measurements of acceleration and angular velocity. In an effort to collate current knowledge and highlight solutions to addressable challenges, in this paper, we report the results of a focused search of research articles using IMUS for kinematic measurements of the shoulder in the free-living environment, discuss the basic steps required for quantifying shoulder motion in the non-laboratory field-based setting using wearable IMUs, and we discuss the challenges that must be overcome in the context of the shoulder joint and the literature review. Finally, we suggest some IMU-based measures that are less sensitive to experimental design and algorithm choices, make recommendations for the information documented in manuscripts describing studies that use IMUs to quantify shoulder motion, and propose directions for future research.

A Hierarchical Bayesian Model for Cyber-Human Assessment of Movement in Upper Extremity Stroke Rehabilitation

The evidence-based quantification of the relation between changes in movement quality and functionality can assist clinicians in achieving more effective structuring or adapting of therapy. In this paper, clinicians rated task, segment, and composite movement feature performance for 478 videos of stroke survivors executing upper extremity therapy tasks. We used the clinician ratings to develop a Hierarchical Bayesian Model (HBM) with task, segment, and composite layers for computing the statistical relation of movement quality changes to function. The model was enhanced through a detailed correlation graph ( ∆HBM ) that links computationally extracted kinematics with clinician-rated composite features for different task-segment combinations. Utilizing the weights and correlation graphs, we finally derive reverse cascading probabilities of the proposed HBM from kinematics to composite features, segments, and tasks. In a test involving 98 cases where clinician ratings differed, the HBM resolved 95% of these discrepancies. The model effectively aligned kinematic data with specific task-segment combinations in over 90% of cases. Once the HBM is expanded and refined through additional data it can be used for the automated calculation of statistical relations between changes in kinematics and performance of functional tasks and the generation of therapy assessment recommendations for clinicians. While our work primarily focuses on the upper extremities of stroke survivors, the HBM can be adapted to many other neurorehabilitation contexts.

Research on motion recognition based on multi-dimensional sensing data and deep learning algorithms

Motion recognition provides movement information for people with physical dysfunction, the elderly and motion-sensing games production, and is important for accurate recognition of human motion. We employed three classical machine learning algorithms and three deep learning algorithm models for motion recognition, namely Random Forests (RF), K-Nearest Neighbors (KNN) and Decision Tree (DT) and Dynamic Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Compared with the Inertial Measurement Unit (IMU) worn on seven parts of body. Overall, the difference in performance among the three classical machine learning algorithms in this study was insignificant. The RF algorithm model performed best, having achieved a recognition rate of 96.67%, followed by the KNN algorithm model with an optimal recognition rate of 95.31% and the DT algorithm with an optimal recognition rate of 94.85%. The performance difference among deep learning algorithm models was significant. The DNN algorithm model performed best, having achieved a recognition rate of 97.71%. Our study validated the feasibility of using multidimensional data for motion recognition and demonstrated that the optimal wearing part for distinguishing daily activities based on multidimensional sensing data was the waist. In terms of algorithms, deep learning algorithms based on multi-dimensional sensors performed better, and tree-structured models still have better performance in traditional machine learning algorithms. The results indicated that IMU combined with deep learning algorithms can effectively recognize actions and provided a promising basis for a wider range of applications in the field of motion recognition.

Validation of 3D Knee Kinematics during Gait on Treadmill with an Instrumented Knee Brace

To test a novel instrumented knee brace intended for use as a rehabilitation system, based on inertial measurement units (IMU) to monitor home-based exercises, the device was compared to the gold standard of motion analysis. The purpose was to validate a new calibration method through functional tasks and assessed the value of adding magnetometers for motion analysis. Thirteen healthy young adults performed a 60-second gait test at a comfortable walking speed on a treadmill. Knee kinematics were captured simultaneously, using the instrumented knee brace and an optoelectronic camera system (OCS). The intraclass correlation coefficient (ICC) showed excellent reliability for the three axes of rotation with and without magnetometers, with values ranging between 0.900 and 0.972. Pearson's r coefficient showed good to excellent correlation for the three axes, with the root mean square error (RMSE) under 3° with the IMUs and slightly higher with the magnetometers. The instrumented knee brace obtained certain clinical parameters, as did the OCS. The instrumented knee brace seems to be a valid tool to assess ambulatory knee kinematics, with an RMSE of <3°, which is sufficient for clinical interpretations. Indeed, this portable system can obtain certain clinical parameters just as well as the gold standard of motion analysis. However, the addition of magnetometers showed no significant advantage in terms of enhancing accuracy.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

Multi-Sensor Data Fusion Approach for Kinematic Quantities

A theoretical framework to implement multi-sensor data fusion methods for kinematic quantities is proposed. All methods defined through the framework allow the combination of signals obtained from position, velocity and acceleration sensors addressing the same target, and improvement in the observation of the kinematics of the target. Differently from several alternative methods, the considered ones need no dynamic and/or error models to operate and can be implemented with low computational burden. In fact, they gain measurements by summing filtered versions of the heterogeneous kinematic quantities. In particular, in the case of position measurement, the use of filters with finite impulse responses, all characterized by finite gain throughout the bandwidth, in place of straightforward time-integrative operators, prevents the drift that is typically produced by the offset and low-frequency noise affecting velocity and acceleration data. A simulated scenario shows that the adopted method keeps the error in a position measurement, obtained indirectly from an accelerometer affected by an offset equal to 1 ppm on the full scale, within a few ppm of the full-scale position. If the digital output of the accelerometer undergoes a second-order time integration, instead, the measurement error would theoretically rise up to 12n(n+1) ppm in the full scale at the n-th discrete time instant. The class of methods offered by the proposed framework is therefore interesting in those applications in which the direct position measurements are characterized by poor accuracy and one has also to look at the velocity and acceleration data to improve the tracking of a target.