Augmenting Sensorimotor Control Using “Goal-Aware” Vibrotactile Stimulation during Reaching and Manipulation Behaviors

We describe two sets of experiments that examine the ability of vibrotactile encoding of simple position error and combined object states (calculated from an optimal controller) to enhance performance of reaching and manipulation tasks in healthy human adults. The goal of the first experiment (tracking) was to follow a moving target with a cursor on a computer screen. Visual and/or vibrotactile cues were provided in this experiment, and vibrotactile feedback was redundant with visual feedback in that it did not encode any information above and beyond what was already available via vision. After only 10 minutes of practice using vibrotactile feedback to guide performance, subjects tracked the moving target with response latency and movement accuracy values approaching those observed under visually guided reaching. Unlike previous reports on multisensory enhancement, combining vibrotactile and visual feedback of performance errors conferred neither positive nor negative effects on task performance. In the second experiment (balancing), vibrotactile feedback encoded a corrective motor command as a linear combination of object states (derived from a linear-quadratic regulator implementing a trade-off between kinematic and energetic performance) to teach subjects how to balance a simulated inverted pendulum. Here, the tactile feedback signal differed from visual feedback in that it provided information that was not readily available from visual feedback alone. Immediately after applying this novel “goal-aware” vibrotactile feedback, time to failure was improved by a factor of three. Additionally, the effect of vibrotactile training persisted after the feedback was removed. These results suggest that vibrotactile encoding of appropriate combinations of state information may be an effective form of augmented sensory feedback that can be applied, among other purposes, to compensate for lost or compromised proprioception as commonly observed, for example, in stroke survivors

Automated classification of bimanual movements in stroke telerehabilitation: a comparison of dimensionality reduction algorithms

Stroke survivors commonly experience unilateral muscle weakness, which limits their engagement in daily activities. Bimanual training has been demonstrated to effectively recover coordinated movements among those patients. We developed a low cost telerehabilitation platform dedicated to bimanual exercise, where the patient manipulates a dowel to control a computer program. Data on movement is collected using a Microsoft Kinect sensor and an inertial measurement unit to interface the platform, as well as to assess motor performance remotely. Toward automatic classification of bimanual movements executed by the user, we test the performance of a linear and a nonlinear dimensionality reduction techniques

MOBEEZE. Natural Interaction Technologies, Virtual Reality and Artificial Intelligence for Gait Disorders Analysis and Rehabilitation in Patients with Parkinson's Disease

Parkinson's Disease (PD) is the most common degenerative disorder after Alzheimer's disease. Generally affecting elderly groups, it has a strong limiting effect on physical functioning and performance of roles, vitality and general perception of health. Since the disease is progressive, the patient knows he's going to get worse. The deterioration is significant not only in mobility but also in pain, social isolation, and emotional reactions. Freezing is a phenomenon associated with this disease and it is characterized by a motor disorder that leaves the patient literally stuck to the ground. Mobeeze is designed with the main objective of providing health personnel with a tool to analyse, evaluate and monitor the progress of patients’ disorders as well as the personalization and adaptation of rehabilitation sessions in patients with Parkinson's disease. Based on the characteristics measured in real time which will allow the strengthening effects of rehabilitation and help to assimilate them in the long term. The creation of Mobeeze allows the constitution of a system of analysis and evaluation of march disorders in real time, through natural interaction, virtual reality and artificial intelligence. In this project, we will analyse if these non-invasive technologies reduce the stress induced to the patient when he is feeling evaluated

Sensor-Based Rehabilitation in Neurological Diseases: A Bibliometric Analysis of Research Trends

Background: As the field of sensor-based rehabilitation continues to expand, it is important to gain a comprehensive understanding of its current research landscape. This study aimed to conduct a bibliometric analysis to identify the most influential authors, institutions, journals, and research areas in this field. Methods: A search of the Web of Science Core Collection was performed using keywords related to sensor-based rehabilitation in neurological diseases. The search results were analyzed with CiteSpace software using bibliometric techniques, including co-authorship analysis, citation analysis, and keyword co-occurrence analysis. Results: Between 2002 and 2022, 1103 papers were published on the topic, with slow growth from 2002 to 2017, followed by a rapid increase from 2018 to 2022. The United States was the most active country, while the Swiss Federal Institute of Technology had the highest number of publications among institutions. Sensors published the most papers. The top keywords included rehabilitation, stroke, and recovery. The clusters of keywords comprised machine learning, specific neurological conditions, and sensor-based rehabilitation technologies. Conclusions: This study provides a comprehensive overview of the current state of sensor-based rehabilitation research in neurological diseases, highlighting the most influential authors, journals, and research themes. The findings can help researchers and practitioners to identify emerging trends and opportunities for collaboration and can inform the development of future research directions in this field

Implementation of Open Source applications “Serious Game” for rehabilitation

Serious Games and Virtual Reality (VR) are present nowadays as an alternative to traditional rehabilitation therapies. This project describes the workflow to develop videogames for health monitoring as well as a source of entertainment for physiotherapy patients, primarily patients that suffer hemiparesis caused by a neurological disease like a stroke. We propose the last version of Microsoft Kinect sensors as low cost game controller and the software Unity to develop Open Source Rehabilitation Serious Games. These Serious Games try to imitate physiotherapy sessions performed in movement recovery therapies, reducing the waiting list of patients together with time and costs to hospitals. The premise is that the gameplay makes patients execute upper body exercises alongside equilibrium training, meanwhile they are monitored extracting useful data and results for the physicians.Ingeniería Biomédic

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

Master of Science

thesisStroke is a leading cause of death and adult disability in the United States. Survivors lose abilities that were controlled by the affected area of the brain. Rehabilitation therapy is administered to help survivors regain control of lost functional abilities. The number of sessions that stroke survivors attend are limited to the availability of a clinic close to their residence and the amount of time friends and family can devote to help them commute, as most are incapable of driving. Home-based therapy using virtual reality and computer games have the potential of solving these issues, increasing the amount of independent therapy performed by patients. This thesis presents the design, development and testing of a low-cost system, potentially suitable for use in the home environment. This system is designed for rehabilitation of the impaired upper limb of stroke survivors. A Microsoft Kinect was used to track the position of the patient's hand and the game requires the user to move the arm over increasing large areas by sliding the arm on a support. Studies were performed with six stroke survivors and five control subjects to determine the feasibility of the system. Patients played the game for 6 to 10 days and their game scores, range of motion and Fugl-Meyer scores were recorded for analysis. Statistically significant (p<0.05) differences were found between the game scores of the first and last day of the study. Furthermore, acceptability surveys revealed patients enjoyed playing the game, found this kind of therapy more enjoyable than conventional therapy and were willing to use the system in the home environment. Future work in the system will be focused on larger studies, improving the comfort of patients while playing the game, and developing new games that address cognitive issues and integrate art and therapy

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

Žestipõhine arvuti kontrollimine kasutades Kinect sensorit

The main goal of the bachelor’s thesis was to develop an application that allows users to interact with a computer without using any intermediate devices that require physical contact. The resulting interface makes it possible to use the functionalities of a Windows desktop relying only on gestures, provided the computer meets the requirements of using a Kinect sensor and is equipped with one. Because the library used to track fingers is still in development, the functionalities are limited at this point. The possibilities for future development in human-computer interaction using vision based hand recognition are endless and the interaction will become more natural and effortless. As the equipment used to recognize hand poses is becoming more affordable and available, the possibility that mechanical devices which need physical contact will become obsolete becomes progressively more of a reality

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application