Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test.

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

An integrated rehabilitation system for the upper limb spasticity assessment and treatment: the Rehabotics passive exoskeletal system.

Limb spasticity is caused by stroke, multiple sclerosis, traumatic brain injury and various central nervous system pathologies such as brain tumors resulting in joint stiffness, loss of hand function and severe pain. This paper presents with the Rehabotics integrated rehabilitation system aiming to provide highly individualized assessment and treatment of the function of the upper limbs for patients with spasticity after stroke, focusing on the developed passive exoskeletal system. The proposed system can: (i) measure various motor and kinematic parameters of the upper limb in order to evaluate the patient's condition and progress, as well as (ii) offer a specialized rehabilitation program (therapeutic exercises, retraining of functional movements and support of daily activities) through an interactive virtual environment. The outmost aim of this multidisciplinary research work is to create new tools for providing high-level treatment and support services to patients with spasticity after stroke.Clinical Relevance- This paper presents a new passive exoskeletal system aiming to provide enhanced treatment and assessment of patients with upper limb spasticity after stroke.

Head mounted display effect on vestibular rehabilitation exercises performance.

Objectives: Vestibular rehabilitation clinical guidelines document the additional benefit offered by the Mixed Reality environments in the reduction of symptoms and the improvement of balance in peripheral vestibular hypofunction. The HOLOBalance platform offers vestibular rehabilitation exercises, in an Augmented Reality (AR) environment, projecting them using a low- cost Head Mounted Display. The effect of the AR equipment on the performance in three of the commonest vestibular rehabilitation exercises is investigated in this pilot study. Methods: Twenty-five healthy adults (12/25 women) participated, executing the predetermined exercises with or without the use of the AR equipment. Results: Statistically significant difference was obtained only in the frequency of head movements in the yaw plane during the execution of a vestibular adaptation exercise by healthy adults (0.97 Hz; 95% CI=(0.56, 1.39), p<0.001). In terms of difficulty in exercise execution, the use of the equipment led to statistically significant differences at the vestibular-oculomotor adaptation exercise in the pitch plane (OR=3.64, 95% CI (-0.22, 7.50), p=0.049), and in the standing exercise (OR=28.28. 95% CI (23.6, 32.96), p=0.0001). Conclusion: Τhe use of AR equipment in vestibular rehabilitation protocols should be adapted to the clinicians' needs.

Automated Assessment of Balance Rehabilitation Exercises With a Data-Driven Scoring Model: Algorithm Development and Validation Study.

BACKGROUND: Balance rehabilitation programs represent the most common treatments for balance disorders. Nonetheless, lack of resources and lack of highly expert physiotherapists are barriers for patients to undergo individualized rehabilitation sessions. Therefore, balance rehabilitation programs are often transferred to the home environment, with a considerable risk of the patient misperforming the exercises or failing to follow the program at all. Holobalance is a persuasive coaching system with the capacity to offer full-scale rehabilitation services at home. Holobalance involves several modules, from rehabilitation program management to augmented reality coach presentation. OBJECTIVE: The aim of this study was to design, implement, test, and evaluate a scoring model for the accurate assessment of balance rehabilitation exercises, based on data-driven techniques. METHODS: The data-driven scoring module is based on an extensive data set (approximately 1300 rehabilitation exercise sessions) collected during the Holobalance pilot study. It can be used as a training and testing data set for training machine learning (ML) models, which can infer the scoring components of all physical rehabilitation exercises. In that direction, for creating the data set, 2 independent experts monitored (in the clinic) 19 patients performing 1313 balance rehabilitation exercises and scored their performance based on a predefined scoring rubric. On the collected data, preprocessing, data cleansing, and normalization techniques were applied before deploying feature selection techniques. Finally, a wide set of ML algorithms, like random forests and neural networks, were used to identify the most suitable model for each scoring component. RESULTS: The results of the trained model improved the performance of the scoring module in terms of more accurate assessment of a performed exercise, when compared with a rule-based scoring model deployed at an early phase of the system (k-statistic value of 15.9% for sitting exercises, 20.8% for standing exercises, and 26.8% for walking exercises). Finally, the resulting performance of the model resembled the threshold of the interobserver variability, enabling trustworthy usage of the scoring module in the closed-loop chain of the Holobalance coaching system. CONCLUSIONS: The proposed set of ML models can effectively score the balance rehabilitation exercises of the Holobalance system. The models had similar accuracy in terms of Cohen kappa analysis, with interobserver variability, enabling the scoring module to infer the score of an exercise based on the collected signals from sensing devices. More specifically, for sitting exercises, the scoring model had high classification accuracy, ranging from 0.86 to 0.90. Similarly, for standing exercises, the classification accuracy ranged from 0.85 to 0.92, while for walking exercises, it ranged from 0.81 to 0.90. TRIAL REGISTRATION: ClinicalTrials.gov NCT04053829; https://clinicaltrials.gov/ct2/show/NCT04053829.

Exploring the Acceptability and Feasibility of Providing a Balance Tele-Rehabilitation Programme to Older Adults at Risk for Falls: An Initial Assessment.

Falls are a major health concern. The HOLOBALANCE tele-rehabilitation system was developed to deliver an evidence based, multi-sensory balance rehabilitation programme, to the elderly at risk of falls. The system delivers a series of balance physiotherapy exercises and cognitive and auditory training tasks prescribed by an expert balance physiotherapist following an initial balance assessment. The HOLOBALANCE system uses augmented reality (AR) to deliver exercises and games, and records task performance via a combination of body worn sensors and a depth camera. The HOLOBALANCE tele-rehabilitation system provides feedback to the supervising clinical team regarding task performance, participant usage and user feedback. Herewith we present the findings from the first 25 study participants regarding the feasibility and acceptability of the proposed system. The results of the clinical study indicate that the system is acceptable by the end users and also feasible for using in hospital and home environments.

Evaluating the Performance of Balance Physiotherapy Exercises Using a Sensory Platform: The Basis for a Persuasive Balance Rehabilitation Virtual Coaching System.

Rehabilitation programs play an important role in improving the quality of life of patients with balance disorders. Such programs are usually executed in a home environment, due to lack of resources. This procedure usually results in poorly performed exercises or even complete drop outs from the programs, as the patients lack guidance and motivation. This paper introduces a novel system for managing balance disorders in a home environment using a virtual coach for guidance, instruction, and inducement. The proposed system comprises sensing devices, augmented reality technology, and intelligent inference agents, which capture, recognize, and evaluate a patient's performance during the execution of exercises. More specifically, this work presents a home-based motion capture and assessment module, which utilizes a sensory platform to recognize an exercise performed by a patient and assess it. The sensory platform comprises IMU sensors (Mbientlab MMR© 9axis), pressure insoles (Moticon©), and a depth RGB camera (Intel D415©). This module is designed to deliver messages both during the performance of the exercise, delivering personalized notifications and alerts to the patient, and after the end of the exercise, scoring the overall performance of the patient. A set of proof of concept validation studies has been deployed, aiming to assess the accuracy of the different components for the sub-modules of the motion capture and assessment module. More specifically, Euler angle calculation algorithm in 2D (R 2 = 0.99) and in 3D (R 2 = 0.82 in yaw plane and R 2 = 0.91 for the pitch plane), as well as head turns speed (R 2 = 0.96), showed good correlation between the calculated and ground truth values provided by experts' annotations. The posture assessment algorithm resulted to accuracy = 0.83, while the gait metrics were validated against two well-established gait analysis systems (R 2 = 0.78 for double support, R 2 = 0.71 for single support, R 2 = 0.80 for step time, R 2 = 0.75 for stride time (WinTrack©), R 2 = 0.82 for cadence, and R 2 = 0.79 for stride time (RehaGait©). Validation results provided evidence that the proposed system can accurately capture and assess a physiotherapy exercise within the balance disorders context, thus providing a robust basis for the virtual coaching ecosystem and thereby improve a patient's commitment to rehabilitation programs while enhancing the quality of the performed exercises. In summary, virtual coaching can improve the quality of the home-based rehabilitation programs as long as it is combined with accurate motion capture and assessment modules, which provides to the virtual coach the capacity to tailor the interaction with the patient and deliver personalized experience.