Assessment Of Rehabilitation Exercises Research Articles

Real-Time Monitoring and Assessment of Rehabilitation Exercises for Low Back Pain through Interactive Dashboard Pose Analysis Using Streamlit—A Pilot Study

In the modern era, AI-driven algorithms have significantly influenced medical diagnosis and therapy. In this pilot study, we propose using Streamlit 1.38.0 to create an interactive dashboard, PoAna .v1—Pose Analysis, as a new approach to address these concerns. In real-time, our system accurately tracks and evaluates individualized rehabilitation exercises for patients suffering from low back pain using features such as exercise visualization and guidance, real-time feedback and monitoring, and personalized exercise plans. This dashboard was very effective for tracking rehabilitation progress. We recruited 32 individuals to participate in this pilot study. We monitored an individual’s overall performance for one week. Of the participants, 18.75% engaged in rehabilitative exercises less frequently than twice daily; 81.25% did so at least three times daily. The proposed Long Short-Term Memory (LSTM) architecture had a training accuracy score of 98.8% and a testing accuracy of 99.7%, with an average accuracy of 10-fold cross-validation of 98.54%. On the pre- and post-test assessments, there is a significant difference between pain levels, with a p < 0.05 and a t-stat value of 12.175. The proposed system’s usability score is 79.375, indicating that it provides a user-friendly environment for the user to use the PoAna .v1 web application. So far, our research suggests that the Streamlit 1.38.0-based dashboard improves patients’ engagement, adherence, and success with exercise. Future research aims to add more characteristics that can improve the complete care of low back pain (LBP) and validate the effectiveness of this intervention in larger patient cohorts.

EGCN++: A New Fusion Strategy for Ensemble Learning in Skeleton-Based Rehabilitation Exercise Assessment.

Skeleton-based exercise assessment focuses on evaluating the correctness or quality of an exercise performed by a subject. Skeleton data provide two groups of features (i.e., position and orientation), which existing methods have not fully harnessed. We previously proposed an ensemble-based graph convolutional network (EGCN) that considers both position and orientation features to construct a model-based approach. Integrating these types of features achieved better performance than available methods. However, EGCN lacked a fusion strategy across the data, feature, decision, and model levels. In this paper, we present an advanced framework, EGCN++, for rehabilitation exercise assessment. Based on EGCN, a new fusion strategy called MLE-PO is proposed for EGCN++; this technique considers fusion at the data and model levels. We conduct extensive cross-validation experiments and investigate the consistency between machine and human evaluations on three datasets: UI-PRMD, KIMORE, and EHE. Results demonstrate that MLE-PO outperforms other EGCN ensemble strategies and representative baselines. Furthermore, the MLE-PO's model evaluation scores are more quantitatively consistent with clinical evaluations than other ensemble strategies.

Rehabilitation exercise quality assessment through supervised contrastive learning with hard and soft negatives.

Exercise-based rehabilitation programs have proven to be effective in enhancing the quality of life and reducing mortality and rehospitalization rates. AI-driven virtual rehabilitation, which allows patients to independently complete exercises at home, utilizes AI algorithms to analyze exercise data, providing feedback to patients and updating clinicians on their progress. These programs commonly prescribe a variety of exercise types, leading to a distinct challenge in rehabilitation exercise assessment datasets: while abundant in overall training samples, these datasets often have a limited number of samples for each individual exercise type. This disparity hampers the ability of existing approaches to train generalizable models with such a small sample size per exercise type. Addressing this issue, this paper introduces a novel supervised contrastive learning framework with hard and soft negative samples that effectively utilizes the entire dataset to train a single model applicable to all exercise types. This model, with a Spatial-Temporal Graph Convolutional Network (ST-GCN) architecture, demonstrated enhanced generalizability across exercises and a decrease in overall complexity. Through extensive experiments on three publicly available rehabilitation exercise assessment datasets, UI-PRMD, IRDS, and KIMORE, our method has proven to surpass existing methods, setting a new benchmark in rehabilitation exercise quality assessment.

LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment

Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial–Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity.

D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation

This paper tackles the challenge of automatically assessing physical rehabilitation exercises for patients who perform the exercises without clinician supervision. The objective is to provide a quality score to ensure correct performance and achieve desired results. To achieve this goal, a new graph-based model, the Dense Spatio-Temporal Graph Conv-GRU Network with Transformer, is introduced. This model combines a modified version of STGCN and transformer architectures for efficient handling of spatio-temporal data. The key idea is to consider skeleton data respecting its non-linear structure as a graph and detecting joints playing the main role in each rehabilitation exercise. Dense connections and GRU mechanisms are used to rapidly process large 3D skeleton inputs and effectively model temporal dynamics. The transformer encoder’s attention mechanism focuses on relevant parts of the input sequence, making it useful for evaluating rehabilitation exercises. The evaluation of our proposed approach on the KIMORE and UI-PRMD datasets highlighted its potential, surpassing state-of-the-art methods in terms of accuracy and computational time. This resulted in faster and more accurate learning and assessment of rehabilitation exercises. Additionally, our model provides valuable feedback through qualitative illustrations, effectively highlighting the significance of joints in specific exercises.

Signal Processing and AI-based Assessment of Rehabilitation Exercises for Diastasis Recti Abdominis

Signal Processing and AI-based Assessment of Rehabilitation Exercises for Diastasis Recti Abdominis

A Skeleton-based Rehabilitation Exercise Assessment System with Rotation Invariance.

Automated exercise assessment is of great importance for patients under rehabilitation exercise who require professional guidance. Among the existing approaches, the skeleton-based assessment model that classifies the correctness of the exercise has attracted much attention due to its relative ease of implementation and convenience in use. However, there are two problems with this approach. The first problem is its sensitivity to the orientation of the human skeleton. To solve this problem, we propose a novel rotation-invariant descriptor, the dot product matrix of the human skeleton, and prove mathematically that this descriptor discards only the orientation message that we do not expect while preserving all other useful information. Lack of feedback from the system is the second problem, because the exercisers do not know which parts of their exercises are incorrect. Therefore, we develop a visualization method for our system based on Gradient-Weighted Class Activation Mapping (Grad-CAM) and an quantitative metric called Overlap Ratio (OvR) to measure the quality of the visualization result. To demonstrate the effect of our method, we conduct experiments on two public datasets and a self-generated push-up dataset. The experimental results show that our rotation-invariant descriptor can achieve absolute robustness to orientation even under severe angle perturbations. In terms of accuracy and OvR, our method even outperforms previous works in most cases, indicating that the rotation-invariant descriptor helps the assessment model to extract more stable features. The visualization results are also informative to correct the movements; some examples are presented in this paper. The code of this paper and our push-up dataset are publicly available at https://github.com/Kelly510/RehabExerAssess.

A Systematic Review of Wearable Sensors for Monitoring Physical Activity.

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking.

Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k -nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments.

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment.

The healthcare industry requires the integration of digital technologies, such as Artificial Intelligence (AI) and the Internet of Things (IoT), to their full potential, particularly during this challenging time and the recent outbreak of the COVID-19 pandemic, which resulted in the disruptions in healthcare delivery, service operations, and shortage of healthcare personnel. However, every opportunity has barriers and bumps, and when it comes to IoT healthcare, data privacy is one of the main growing issues. Despite the recent advances in the development of IoT healthcare architectures, most of them are invasive for the data subjects. In this context, the broad applications of AI in the IoT domain have also been hindered by emerging strict legal and ethical requirements to protect individual privacy. Camera-based solutions that monitor human subjects in everyday settings, e.g., for Online Range of Motion (ROM) detection, are making this problem even worse. One actively practiced branch of such solutions is telerehabilitation, which provides remote solutions for the physically impaired to regain their strength and get back to their normal daily routines. The process usually involves transmitting video/images from the patient performing rehabilitation exercises and applying Machine Learning (ML) techniques to extract meaningful information to help therapists devise further treatment plans. Thereby, real-time measurement and assessment of rehabilitation exercises in a reliable, accurate, and Privacy-Preserving manner is imperative. To address the privacy issue of existing solutions, this paper proposes a holistic Privacy-Preserving (PP) hierarchical IoT solution that simultaneously addresses the utilization of AI-driven IoT and the demands for data protection. Furthermore, the efficiency of the proposed architecture is demonstrated by a novel machine learning-based system that allows immediate assessment and extraction of ROM as the critical information for analyzing the progress of patients.

Solution-Processed Sensing Textiles with Adjustable Sensitivity and Linear Detection Range Enabled by Twisting Structure.

Wearable strain sensors are emerging rapidly for their promising applications in human motion detection for diagnosis, healthcare, training instruction, and rehabilitation exercise assessment. However, it remains a bottleneck in gaining comfortable and breathable devices with the features of high sensitivity, linear response, and tunable detection range. Textiles possess fascinating advantages of good breathability, aesthetic property, tailorability, and excellent mechanical compliance to conformably attach to human body. As the meandering loops in a textile can be extended in different directions, it provides plenty of room for exploring ideal sensors by tuning a twisting structure with rationally selected yarn materials. Herein, textile sensors with twisting architecture are designed via a solution-based process by using a stable water-based conductive ink that is composed of polypyrrole/polyvinyl alcohol nanoparticles with a mean diameter of 50 nm. Depending on the predesigned twisting models, the thus-fabricated textile sensors show adjustable performances, exhibiting a high sensitivity of 38.9 with good linearity and a broad detection range of 200%. Such sensors can be integrated into fabrics and conformably attached to skin for monitoring subtle (facial expressions, breathing, and speaking) and large (stretching, jumping, running and jogging, and sign language) human motions. As a proof-of-concept application, by integrating with a wireless transmitter, the signals detected by our sensors during exercise (e.g., running) can be remotely received and displayed on a smartphone. It is believed that the integration of our textile sensors with selected twisting models into a cloth promises full-range motion detection for wearable electronics and human-machine interfaces.

A Hidden Semi-Markov Model based approach for rehabilitation exercise assessment

In this paper, a Hidden Semi-Markov Model (HSMM) based approach is proposed to evaluate and monitor body motion during a rehabilitation training program. The approach extracts clinically relevant motion features from skeleton joint trajectories, acquired by the RGB-D camera, and provides a score for the subject’s performance. The approach combines different aspects of rule and template based methods. The features have been defined by clinicians as exercise descriptors and are then assessed by a HSMM, trained upon an exemplar motion sequence. The reliability of the proposed approach is studied by evaluating its correlation with both a clinical assessment and a Dynamic Time Warping (DTW) algorithm, while healthy and neurological disabled people performed physical exercises. With respect to the discrimination between healthy and pathological conditions, the HSMM based method correlates better with the physician’s score than DTW. The study supports the use of HSMMs to assess motor performance providing a quantitative feedback to physiotherapist and patients. This result is particularly appropriate and useful for a remote assessment in the home.

Wearable sensor-based rehabilitation exercise assessment for knee osteoarthritis.

Since the knee joint bears the full weight load of the human body and the highest pressure loads while providing flexible movement, it is the body part most vulnerable and susceptible to osteoarthritis. In exercise therapy, the early rehabilitation stages last for approximately six weeks, during which the patient works with the physical therapist several times each week. The patient is afterwards given instructions for continuing rehabilitation exercise by him/herself at home. This study develops a rehabilitation exercise assessment mechanism using three wearable sensors mounted on the chest, thigh and shank of the working leg in order to enable the patients with knee osteoarthritis to manage their own rehabilitation progress. In this work, time-domain, frequency-domain features and angle information of the motion sensor signals are used to classify the exercise type and identify whether their postures are proper or not. Three types of rehabilitation exercise commonly prescribed to knee osteoarthritis patients are: Short-Arc Exercise, Straight Leg Raise, and Quadriceps Strengthening Mini-squats. After ten subjects performed the three kinds of rehabilitation activities, three validation techniques including 10-fold cross-validation, within subject cross validation, and leave-one-subject cross validation are utilized to confirm the proposed mechanism. The overall recognition accuracy for exercise type classification is 97.29% and for exercise posture identification it is 88.26%. The experimental results demonstrate the feasibility of the proposed mechanism which can help patients perform rehabilitation movements and progress effectively. Moreover, the proposed mechanism is able to detect multiple errors at once, fulfilling the requirements for rehabilitation assessment.

Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study.

BackgroundAccurate assessments of adherence and exercise performance are required in order to ensure that patients adhere to and perform their rehabilitation exercises correctly within the home environment. Inertial sensors have previously been advocated as a means of achieving these requirements, by using them as an input to an exercise biofeedback system. This research sought to investigate whether inertial sensors, and in particular a single sensor, can accurately classify exercise performance in patients performing lower limb exercises for rehabilitation purposes.MethodsFifty-eight participants (19 male, 39 female, age: 53.9 ± 8.5 years, height: 1.69 ± 0.08 m, weight: 74.3 ± 13.0 kg) performed ten repetitions of seven lower limb exercises (hip abduction, hip flexion, hip extension, knee extension, heel slide, straight leg raise, and inner range quadriceps). Three inertial sensor units, secured to the thigh, shin and foot of the leg being exercised, were used to acquire data during each exercise. Machine learning classification methods were applied to quantify the acquired data.ResultsThe classification methods achieved relatively high accuracy at distinguishing between correct and incorrect performance of an exercise using three, two, or one sensor while moderate efficacy scores were also achieved by the classifier when attempting to classify the particular error in exercise performance. Results also illustrated that a reduction in the number of inertial sensor units employed has little effect on the overall efficacy results.ConclusionThe results revealed that it is possible to classify lower limb exercise performance using inertial sensors with satisfactory levels of accuracy and reducing the number of sensors employed does not reduce the accuracy of the method.Electronic supplementary materialThe online version of this article (doi:10.1186/1743-0003-11-158) contains supplementary material, which is available to authorized users.

A tool for linguistic assessment of rehabilitation exercises

In this paper, human motion analysis is performed by modeling a physical complex exercise in order to provide feedback about the patient's performance in rehabilitation therapies.The Sun Salutation exercise, which is a flowing sequence of 12 yoga poses, is analyzed. This exercise provides physical benefits as improving the strength and flexibility of the muscles and the alignment of the spinal column. A temporal series of measures that contains a numerical description of this sequence is obtained by using a wearable sensing system for monitoring, which is formed by five high precision tri-axial accelerometer sensors worn by the patient while performing the exercise.Due to the complexity of the exercise and the huge amount of available data, its interpretation is a challenging task. Therefore, this paper describes the design of a computational system able of interpreting and generating linguistic descriptions about this exercise. Previous works on both Granular Linguistic Models of Phenomena and Fuzzy Finite State Machines are used to create a basic linguistic model of the Sun Salutation. This model allows generating human friendly reports focused on the assessment of the exercise quality based on its symmetry, stability and rhythm.