A low-cost virtual coach for 2D video-based compensation assessment of upper extremity rehabilitation exercises

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture

This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using 'filtered' estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model's efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency.

Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy

Shoulder pain represents the most frequently reported musculoskeletal disorder, often leading to significant functional impairment and pain, impacting quality of life. Home-based rehabilitation programs offer a more accessible and convenient solution for an effective shoulder disorder treatment, addressing logistical and financial constraints associated with traditional physiotherapy. The aim of this systematic review is to report the monitoring devices currently proposed and tested for shoulder rehabilitation in home settings. The research question was formulated using the PICO approach, and the PRISMA guidelines were applied to ensure a transparent methodology for the systematic review process. A comprehensive search of PubMed and Scopus was conducted, and the results were included from 2014 up to 2023. Three different tools (i.e., the Rob 2 version of the Cochrane risk-of-bias tool, the Joanna Briggs Institute (JBI) Critical Appraisal tool, and the ROBINS-I tool) were used to assess the risk of bias. Fifteen studies were included as they fulfilled the inclusion criteria. The results showed that wearable systems represent a promising solution as remote monitoring technologies, offering quantitative and clinically meaningful insights into the progress of individuals within a rehabilitation pathway. Recent trends indicate a growing use of low-cost, non-intrusive visual tracking devices, such as camera-based monitoring systems, within the domain of tele-rehabilitation. The integration of home-based monitoring devices alongside traditional rehabilitation methods is acquiring significant attention, offering broader access to high-quality care, and potentially reducing healthcare costs associated with in-person therapy.

Age- and sex-related differences in upper-body joint and endpoint kinematics during a drinking task in healthy adults

Background

The objective kinematic assessments of activities of daily living are desired. However, the limited knowledge regarding age- and sex-related differences prevents the adaptation of these measurements to clinical settings and in-home exercises. Therefore, this study aimed to determine the effects of age and sex on joint and endpoint kinematics during a common activity of daily living, specifically, drinking from a glass.

Methods

In total, 32 healthy adults (18 males and 14 females) aged 22-77 years performed a drinking task comprising reaching for a glass, bringing it forward and sipping, returning it, and placing the hand back to the starting position, which was recorded using a three-dimensional motion-capturing system. A two-way analysis of variance was used to statistically compare joint angles at five different time points and endpoint kinematic variables in the four drinking phases between older and younger age groups and sexes.

Results

Wrist radial deviation was greater in older adults than in younger participants at all five different time points (F = 5.16-7.34, p ≤ 0.03, η2 = 0.14-0.21). Moreover, lesser shoulder abduction and greater shoulder internal rotation and forearm pronation when moving and returning the hand to the starting position were observed in the female group than in the male group (F = 4.21-20.03, p ≤ 0.0497, η2 = 0.13-0.41). Trunk flexion was lower in the female group than in the male group at all time points (F = 4.25-7.13, p ≤ 0.0485, η2 = 0.12-0.19). Regarding endpoint kinematics, the performance time in the reaching phase was longer in older adults than in younger individuals (F = 4.96, p = 0.03, η2 = 0.14). Furthermore, a shorter time while returning the hand to the starting position was observed in the female group than in the male group (F = 9.55, p < 0.01, η2 = 0.22).

Conclusions

The joint kinematics of drinking were partially characterized by an age effect, whereas endpoint kinematics were scattered in all drinking phases. Sex-related effects in most upper-body motions and postures may cause rapid motions in females. Therefore, clinicians could use this knowledge for precise assessments and to suggest feasible in-home exercises.

A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions

In the domain of physical rehabilitation, the progress in machine learning and the availability of cost-effective motion capture technologies have paved the way for innovative systems capable of capturing human movements, automatically analyzing recorded data, and evaluating movement quality. This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired. The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns. By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.