152 research outputs found
Home-based physical therapy with an interactive computer vision system
In this paper, we present ExerciseCheck. ExerciseCheck is an interactive computer vision system that is sufficiently modular to work with different sources of human pose estimates, i.e., estimates from deep or traditional models that
interpret RGB or RGB-D camera input. In a pilot study, we first compare the pose estimates produced by four deep models based on RGB input with those of the MS Kinect based on RGB-D data. The results indicate a performance
gap that required us to choose the MS Kinect when we tested ExerciseCheck with Parkinson’s disease patients in their homes. ExerciseCheck is capable of customizing exercises, capturing exercise information, evaluating patient performance, providing therapeutic feedback to the patient and the therapist, checking the progress of the user over the course of the physical therapy, and supporting the patient
throughout this period. We conclude that ExerciseCheck is a user-friendly computer vision application that can assist patients by providing motivation and guidance to ensure correct execution of the required exercises. Our results also suggest that while there has been considerable progress in the field of pose estimation using deep learning, current deep learning models are not fully ready to replace
RGB-D sensors, especially when the exercises involved are complex, and the patient population being accounted for has to be carefully tracked for its “active range of motion.”Published versio
Markerless Vision-Based Skeleton Tracking in Therapy of Gross Motor Skill Disorders in Children
This chapter presents a research towards implementation of a computer vision system for markerless skeleton tracking in therapy of gross motor skill disorders in children suffering from mild cognitive impairment. The proposed system is based on a low-cost 3D sensor and a skeleton tracking software. The envisioned architecture is scalable in the sense that the system may be used as a stand-alone assistive tool for tracking the effects of therapy or it may be integrated with an advanced autonomous conversational agent to maintain the spatial attention of the child and to increase her motivation to undergo a long-term therapy
Low-Cost Sensors and Biological Signals
Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization
Technology assisted screening and balance training systems for stroke patients
by Deepesh KumarPh.D
Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement
As life expectancy is mostly increasing, the incidence of many neurological
disorders is also constantly growing. For improving the physical functions
affected by a neurological disorder, rehabilitation procedures are mandatory,
and they must be performed regularly. Unfortunately, neurorehabilitation
procedures have disadvantages in terms of costs, accessibility and a lack of
therapists. This paper presents Immersive Neurorehabilitation Exercises Using
Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system
using virtual reality. The system is based on a thorough research methodology
and is able to capture real-time user movements and evaluate joint mobility for
both upper and lower limbs, record training sessions and save electromyography
data. The use of the first-person perspective increases immersion, and the
joint range of motion is calculated with the help of both the HTC Vive system
and inverse kinematics principles applied on skeleton rigs. Tutorial exercises
are demonstrated by a virtual therapist, as they were recorded with real-life
physicians, and sessions can be monitored and configured through tele-medicine.
Complex movements are practiced in gamified settings, encouraging
self-improvement and competition. Finally, we proposed a training plan and
preliminary tests which show promising results in terms of accuracy and user
feedback. As future developments, we plan to improve the system's accuracy and
investigate a wireless alternative based on neural networks.Comment: 47 pages, 20 figures, 17 tables (including annexes), part of the MDPI
Sesnsors "Special Issue Smart Sensors and Measurements Methods for Quality of
Life and Ambient Assisted Living
Development of a 3D, networked multi-user virtual reality environment for home therapy after stroke
Abstract
Background
Impairment of upper extremity function is a common outcome following stroke, to the detriment of lifestyle and employment opportunities. Yet, access to treatment may be limited due to geographical and transportation constraints, especially for those living in rural areas. While stroke rates are higher in these areas, stroke survivors in these regions of the country have substantially less access to clinical therapy. Home therapy could offer an important alternative to clinical treatment, but the inherent isolation and the monotony of self-directed training can greatly reduce compliance.
Methods
We developed a 3D, networked multi-user Virtual Environment for Rehabilitative Gaming Exercises (VERGE) system for home therapy. Within this environment, stroke survivors can interact with therapists and/or fellow stroke survivors in the same virtual space even though they may be physically remote. Each user’s own movement controls an avatar through kinematic measurements made with a low-cost, Kinect™ device. The system was explicitly designed to train movements important to rehabilitation and to provide real-time feedback of performance to users and clinicians. To obtain user feedback about the system, 15 stroke survivors with chronic upper extremity hemiparesis participated in a multisession pilot evaluation study, consisting of a three-week intervention in a laboratory setting. For each week, the participant performed three one-hour training sessions with one of three modalities: 1) VERGE system, 2) an existing virtual reality environment based on Alice in Wonderland (AWVR), or 3) a home exercise program (HEP).
Results
Over 85% of the subjects found the VERGE system to be an effective means of promoting repetitive practice of arm movement. Arm displacement averaged 350 m for each VERGE training session. Arm displacement was not significantly less when using VERGE than when using AWVR or HEP. Participants were split on preference for VERGE, AWVR or HEP. Importantly, almost all subjects indicated a willingness to perform the training for at least 2–3 days per week at home.
Conclusions
Multi-user VR environments hold promise for home therapy, although the importance of reducing complexity of operation for the user in the VR system must be emphasized. A modified version of the VERGE system is currently being used in a home therapy study
A RGBD-Based interactive system for gaming-driven rehabilitation of upper limbs
Current physiotherapy services may not be effective or suitable for certain patients due to lack of motivation, poor adherence to exercises, insufficient supervision and feedback or, in the worst case, refusal to continue with the rehabilitation plan. This paper introduces a novel approach for rehabilitation of upper limbs through KineActiv, a platform based on Microsoft Kinect v2 and developed in Unity Engine. KineActiv proposes exergames to encourage patients to perform rehabilitation exercises prescribed by a specialist, controls the patient's performance, and corrects execution errors on the fly. KineActiv comprises a web platform where the physiotherapist can review session results, monitor patient health, and adjust rehabilitation routines. We recruited 10 patients for assessing the system usability as well as the system performance. Results show that KineActiv is a usable, enjoyable and reliable system, that does not cause any negative feelings
Data analytics for image visual complexity and kinect-based videos of rehabilitation exercises
With the recent advances in computer vision and pattern recognition, methods from these fields are successfully applied to solve problems in various domains, including health care and social sciences. In this thesis, two such problems, from different domains, are discussed. First, an application of computer vision and broader pattern recognition in physical therapy is presented. Home-based physical therapy is an essential part of the recovery process in which the patient is prescribed specific exercises in order to improve symptoms and daily functioning of the body. However, poor adherence to the prescribed exercises is a common problem. In our work, we explore methods for improving home-based physical therapy experience. We begin by proposing DyAd, a dynamically difficulty adjustment system which captures the trajectory of the hand movement, evaluates the user's performance quantitatively and adjusts the difficulty level for the next trial of the exercise based on the performance measurements. Next, we introduce ExerciseCheck, a remote monitoring and evaluation platform for home-based physical therapy. ExerciseCheck is capable of capturing exercise information, evaluating the performance, providing therapeutic feedback to the patient and the therapist, checking the progress of the user over the course of the physical therapy, and supporting the patient throughout this period. In our experiments, Parkinson patients have tested our system at a clinic and in their homes during their physical therapy period. Our results suggests that ExerciseCheck is a user-friendly application and can assist patients by providing motivation, and guidance to ensure correct execution of the required exercises.
As the second application, and within computer vision paradigm, we focus on visual complexity, an image attribute that humans can subjectively evaluate based on the level of details in the image. Visual complexity has been studied in psychophysics, cognitive science, and, more recently, computer vision, for the purposes of product design, web design, advertising, etc. We first introduce a diverse visual complexity dataset which compromises of seven image categories. We collect the ground-truth scores by comparing the pairwise relationship of images and then convert the pairwise scores to absolute scores using mathematical methods. Furthermore, we propose a method to measure the visual complexity that uses unsupervised information extraction from intermediate convolutional layers of deep neural networks. We derive an activation energy metric that combines convolutional layer activations to quantify visual complexity. The high correlations between ground-truth labels and computed energy scores in our experiments show superiority of our method compared to the previous works. Finally, as an example of the relationship between visual complexity and other image attributes, we demonstrate that, within the context of a category, visually more complex images are more memorable to human observers
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