Falls prevention advice and visual feedback to those at risk of falling : study protocol for a pilot randomized controlled trial

Studies have shown that functional strength and balance exercises can reduce the risk of falling in older people if they are done on a regular basis. However, the repetitive nature of these exercises; combined with the inherent lack of feedback of progress may discourage seniors from exercising in the home, thereby rendering such an intervention ineffective. This study hypothesizes that the use of visual feedback and multimodal games will be more effective in encouraging adherence to home rehabilitation than standard care; thereby promoting independence and improving the quality of life in older adults at risk of falling

Development and preliminary evaluation of a novel low cost VR-based upper limb stroke rehabilitation platform using Wii technology.

Abstract Purpose: This paper proposes a novel system (using the Nintendo Wii remote) that offers customised, non-immersive, virtual reality-based, upper-limb stroke rehabilitation and reports on promising preliminary findings with stroke survivors. Method: The system novelty lies in the high accuracy of the full kinematic tracking of the upper limb movement in real-time, offering strong personal connection between the stroke survivor and a virtual character when executing therapist prescribed adjustable exercises/games. It allows the therapist to monitor patient performance and to individually calibrate the system in terms of range of movement, speed and duration. Results: The system was tested for acceptability with three stroke survivors with differing levels of disability. Participants reported an overwhelming connection with the system and avatar. A two-week, single case study with a long-term stroke survivor showed positive changes in all four outcome measures employed, with the participant reporting better wrist control and greater functional use. Activities, which were deemed too challenging or too easy were associated with lower scores of enjoyment/motivation, highlighting the need for activities to be individually calibrated. Conclusions: Given the preliminary findings, it would be beneficial to extend the case study in terms of duration and participants and to conduct an acceptability and feasibility study with community dwelling survivors. Implications for Rehabilitation Low-cost, off-the-shelf game sensors, such as the Nintendo Wii remote, are acceptable by stroke survivors as an add-on to upper limb stroke rehabilitation but have to be bespoked to provide high-fidelity and real-time kinematic tracking of the arm movement. Providing therapists with real-time and remote monitoring of the quality of the movement and not just the amount of practice, is imperative and most critical for getting a better understanding of each patient and administering the right amount and type of exercise. The ability to translate therapeutic arm movement into individually calibrated exercises and games, allows accommodation of the wide range of movement difficulties seen after stroke and the ability to adjust these activities (in terms of speed, range of movement and duration) will aid motivation and adherence - key issues in rehabilitation. With increasing pressures on resources and the move to more community-based rehabilitation, the proposed system has the potential for promoting the intensity of practice necessary for recovery in both community and acute settings.The National Health Service (NHS) London Regional Innovation Fund

Game-Assisted Rehabilitation for Post-Stroke Survivors

Stroke is a leading cause of permanent impairments among its survivors. Although patients need to go through intensive, longitudinal rehabilitation to regain function before the stroke, patients show poor engagement and adherence to rehabilitation therapies which hampers their recovery. As a means to enhance stroke survivors\u27 motivation, engagement, and adherence to intensive and longitudinal rehabilitation, the use of games in stroke rehabilitation has received attention from research and clinical communities. In order to realize this, it is important to take a holistic, end-to-end research approach that encompasses 1) the development of game technologies that are not only entertaining but also rehabilitating or monitoring the functional/impairment level, 2) the quantitative evaluation of the clinical efficacy of the technologies, and 3) the deployment of the technologies in real-world clinical settings to understand if the anticipated clinical efficacy is achieved and how the technologies affect the interaction dynamics among patients, therapists and the technologies. In this dissertation, we introduce our approach to this holistic, end-to-end research for the development, validation, and understanding of real-world use of games in stroke rehabilitation. Towards that end, we designed and developed Neuro-World that can enable accurate, longitudinal assessment of patients\u27 cognitive function outside the clinical setting. The analysis results in the controlled setting show that the proposed approach can accurately assess patients\u27 cognitive function without the help of therapists. While it remains as a future study to deploy Neuro-World in stroke survivors\u27 free-living environments and study the system\u27s impacts on patients\u27 engagement patterns, we use another game platform (i.e., RAPAEL Smart Board) to demonstrate our efforts to understand the real-world interaction dynamics. We investigate the use of the games in the actual hospital setting where the system has been adopted as part of stroke survivors\u27 routine upper-limb motor rehabilitation therapies. Our findings reveal patients’ unexpected engagement patterns, therapists’ critical roles and challenges in maintaining patients\u27 engagement and therapeutic value. These findings enhance our understanding of the impact of serious games on the patients\u27 engagement to rehabilitation therapies and provide us with insights into potential directions leading to the development of more effective games for stroke rehabilitation

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence

Important Parameters for Hand Function Assessment of Stroke Patients

Clinical scales such as Fugl-Meyer Assessment and Motor Assessment Scale are widely used to evaluate stroke patient's motor performance. However, the scoring systems of these assessments provide only rough estimation, making it difficult to objectively quantify impairment and disability or even rehabilitation progress throughout their rehabilitation period. In contrast, robot-based assessments are objective, repeatable, and could potentially reduce the assessment time. However, robot-based assessment scales are not as well established as conventional assessment scale and the correlation to conventional assessment scale is unclear. This paper discusses the important parameters in order to assess the hand function of stroke patients. This knowledge will provide a contribution to the development of a new robot-based assessment device effectively by including the important parameters in the device. The important parameters were included in development of iRest and yielded promising results that illustrate the potential of the important parameters in assessing the hand function of stroke patients

Kinematic assessment for stroke patients in a stroke game and a daily activity recognition and assessment system

Stroke is the leading cause of serious, long-term disabilities among which deficits in motor abilities in arms or legs are most common. Those who suffer a stroke can recover through effective rehabilitation which is delicately personalized. To achieve the best personalization, it is essential for clinicians to monitor patients' health status and recovery progress accurately and consistently. Traditionally, rehabilitation involves patients performing exercises in clinics where clinicians oversee the procedure and evaluate patients' recovery progress. Following the in-clinic visits, additional home practices are tailored and assigned to patients. The in-clinic visits are important to evaluate recovery progress. The information collected can then help clinicians customize home practices for stroke patients. However, as the number of in-clinic sessions is limited by insurance policies, the recovery information collected in-clinic is often insufficient. Meanwhile, the home practice programs report low adherence rates based on historic data. Given that clinicians rely on patients to self-report adherence, the actual adherence rate could be even lower. Despite the limited feedback clinicians could receive, the measurement method is subjective as well. In practice, classic clinical scales are mostly used for assessing the qualities of movements and the recovery status of patients. However, these clinical scales are evaluated subjectively with only moderate inter-rater and intra-rater reliabilities. Taken together, clinicians lack a method to get sufficient and accurate feedback from patients, which limits the extent to which clinicians can personalize treatment plans. This work aims to solve this problem. To help clinicians obtain abundant health information regarding patients' recovery in an objective approach, I've developed a novel kinematic assessment toolchain that consists of two parts. The first part is a tool to evaluate stroke patients' motions collected in a rehabilitation game setting. This kinematic assessment tool utilizes body-tracking in a rehabilitation game. Specifically, a set of upper body assessment measures were proposed and calculated for assessing the movements using skeletal joint data. Statistical analysis was applied to evaluate the quality of upper body motions using the assessment outcomes. Second, to classify and quantify home activities for stroke patients objectively and accurately, I've developed DARAS, a daily activity recognition and assessment system that evaluates daily motions in a home setting. DARAS consists of three main components: daily action logger, action recognition part, and assessment part. The logger is implemented with a Foresite system to record daily activities using depth and skeletal joint data. Daily activity data in a realistic environment were collected from sixteen post-stroke participants. The collection period for each participant lasts three months. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network fuses the prediction outputs from customized 3D Convolutional-De-Convolutional, customized Region Convolutional 3D network and a proposed Region Hierarchical Co-occurrence network which learns rich spatial-temporal features from either depth data or joint data. The per-frame precision and the per-action precision were 0.819 and 0.838, respectively, on the validation set. For the recognized actions, the kinematic assessments were performed using the skeletal joint data, as well as the longitudinal assessments. The results showed that, compared with non-stroke participants, stroke participants had slower hand movements, were less active, and tended to perform fewer hand manipulation actions. The assessment outcomes from the proposed toolchain help clinicians to provide more personalized rehabilitation plans that benefit patients.Includes bibliographical references

Rehabilitation Robotics and Machine Learning for Stroke Severity Classification

Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. This study uses supervised learning methods to address an autonomous classification via stroke severity labeled data by a clinician. Thirty-three patients with chronic stroke performed a variety of rehabilitation activities while utilizing the Motus Nova rehabilitation technology to capture upper and lower body motion. Based on the minimum, maximum, and mean of the range of motion and pressure as well as the number of movements, force flexion, and extension for each game and session provided from the sensor data. Supervised learning methods were applied to a harmonized dataset of roughly 32,000 patient sessions based on the maximum score per session per game. With this approach using light gradient boosting methods we achieved an average of 94% accuracy with 10-fold cross-validation to prevent overfitting. This thesis shows objectively-measured rehabilitation training, enabling the identification of the stroke severity class with the hopes to have patients have a less severe class in the future. Over the last 10 years robotic rehabilitation has been utilized in inpatient therapy. Robotic rehabilitation has been shown to be effective in improving the severity of stroke in some cases. In particular, robotic devices can be used to help stroke survivors regain movement, improve their functional abilities and improve depression (11). These devices can provide a high level of precision and repeatability, allowing patients to perform ther- apeutic exercises with greater accuracy and consistency (1). Additionally, because robotic devices can be programmed to provide different levels of assistance, they can be tailored to the individual needs of each patient. This allows for a more personalized and effective rehabilitation in-home program (21)

The role of virtual motor rehabilitation: a quantitative analysis between acute and chronic patients with acquired brain injury

"(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Acquired brain injury (ABI) is one of the main problems of disability and death in the world. Its incidence and survival rate are increasing annually. Thus, the number of chronic ABI patients is gradually growing. Traditionally, rehabilitation programs are applied to postacute and acute patients, but recent publications determine that chronic patients may benefit from rehabilitation. Also, in the last few years, the potential of virtual rehabilitation (VR) systems has been demonstrated. However, until now, no previous studies have been carried out to compare the evolution of chronic patients with acute patients in a VR program. To perform this study, we developed a VR system for ABI patients. The system, vestibular virtual rehabilitation (V2R), was designed with clinical specialists. V2R has been tested with 21 people ranging in age from 18 to 80 years old that were classified in two groups: chronic patients and acute patients. The results demonstrate a similar recovery for chronic and acute patients during the intervention period. Also, the results showed that chronic patients stop their improvement when they finish their training. This conclusion encourages us to direct our developments toward VR systems that can be easily integrated at home, allowing chronic patients to have a permanent VR training program.This work was supported by the Ministerio de Educacion y Ciencia Spain: Projects Consolider-C (SEJ2006-14301/PSIC), "CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII," and the Excellence Research Program PROMETEO (Generalitat Valenciana. Conselleria de Educacion, 2008-157).Albiol Pérez, S.; Gil-Gómez, J.; Llorens Rodríguez, R.; Alcañiz Raya, ML.; Colomer Font, C. (2014). The role of virtual motor rehabilitation: a quantitative analysis between acute and chronic patients with acquired brain injury. IEEE Journal of Biomedical and Health Informatics. 18(1):391-398. https://doi.org/10.1109/JBHI.2013.2272101S39139818

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Design and Development of ReMoVES Platform for Motion and Cognitive Rehabilitation

Exergames have recently gained popularity and scientific reliability in the field of assistive computing technology for human well-being. The ReMoVES platform, developed by the author, provides motor and cognitive exergames to be performed by elderly or disabled people, in conjunction with traditional rehabilitation. Data acquisition during the exercise takes place through Microsoft Kinect, Leap Motion and touchscreen monitor. The therapist is provided with feedback on patients' activity over time in order to assess their weakness and correct inaccurate movement attitudes. This work describes the technical characteristics of the ReMoVES platform, designed to be used by multiple locations such as rehabilitation centers or the patient's home, while providing a centralized data collection server. The system includes 15 exergames, developed from scratch by the author, with the aim of promoting motor and cognitive activity through patient entertainment. The ReMoVES platform differs from similar solutions for the automatic data processing features in support of the therapist. Three methods are presented: based on classic data analysis, on Support Vector Machine classification, and finally on Recurrent Neural Networks. The results describe how it is possible to discern patient gaming sessions with adequate performance from those with incorrect movements with an accuracy of up to 92%. The system has been used with real patients and a data database is made available to the scientific community. The aim is to encourage the dissemination of such data to lay the foundations for a comparison between similar studies