Wearable inertial sensors and range of motion metrics in physical therapy remote support

Abstract. The practice of physiotherapy diagnoses patient ailments which are often treated by the daily repetition of prescribed physiotherapeutic exercise. The effectiveness of the exercise regime is dependent on regular daily repetition of the regime and the correct execution of the prescribed exercises. Patients often have issues learning unfamiliar exercises and performing the exercise with good technique. This design science research study examines a back squat classifier design to appraise patient exercise regime away from the physiotherapy practice. The scope of the exercise appraisal is limited to one exercise, the back squat. Kinematic data captured with commercial inertial sensors is presented to a small group of physiotherapists to illustrate the potential of the technology to measure range of motion (ROM) for back squat appraisal. Opinions are considered from two fields of physiotherapy, general musculoskeletal and post-operative rehabilitation. While the exercise classifier is considered not suitable for post-operative rehabilitation, the opinions expressed for use in general musculoskeletal physiotherapy are positive. Kinematic data captured with gyroscope sensors in the sagittal plane is analysed with Matlab to develop a method for back squat exercise recognition and appraisal. The artefact, a back squat classifier with appraisal features is constructed from Matlab scripts which are proven to be effective with kinematic data from a novice athlete

Method for the acquisition of arm movement data using accelerometers

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (p. 37).Partial paralysis is one of the most common problems that affect stroke survivors. Many different rehabilitation therapies are available to stroke patients, including robot-aided rehabilitation, immobilization therapy, and electrical stimulation. Regardless of the choice of therapy, it is beneficial for the therapist to know whether the therapy is improving the patient's functional use of the impaired limb in daily activity. The goal of this project is to develop a method for using accelerometers to monitor and quantify the amount of motion in the arm, for the application of monitoring limb use in stroke patients outside of therapy sessions. Two analysis methods were designed. The first was based on the kinematics of the arm. The second was based on angular accelerations and the related forces applied to the shoulder and elbow joints. The two methods were tested on samples of different movement, which were chosen to represent the general motion of daily activities. The methods were tested to determine their accuracy at counting the number of movements that occurred, and their ability to produce activity values as an indication of the amplitude of the movements. The two analysis methods which were developed can identify movement of the arm under the conditions which were tested.Thus, it appears that acceleration values can be processed to monitor and quantify arm motion. With future investigation into analyzing areas that were not tested by this project, these methods hold potential to be applied to using accelerometers to monitor arm use of patients while they are receiving rehabilitation therapy.by Allison L. Hall.S.B

AI Modeling Approaches for Detecting, Characterizing, and Predicting Brief Daily Behaviors such as Toothbrushing using Wrist Trackers.

Continuous advancements in wrist-worn sensors have opened up exciting possibilities for real-time monitoring of individuals\u27 daily behaviors, with the aim of promoting healthier, more organized, and efficient lives. Understanding the duration of specific daily behaviors has become of interest to individuals seeking to optimize their lifestyles. However, there is still a research gap when it comes to monitoring short-duration behaviors that have a significant impact on health using wrist-worn inertial sensors in natural environments. These behaviors often involve repetitive micro-events that last only a few seconds or even microseconds, making their detection and analysis challenging. Furthermore, these micro-events are often surrounded by non-repetitive boundary events, further complicating the identification process. Effective detection and timely intervention during these short-duration behaviors are crucial for designing personalized interventions that can positively impact individuals\u27 lifestyles. To address these challenges, this dissertation introduces three models: mORAL, mTeeth, and Brushing Prompt. These models leverage wrist-worn inertial sensors to accurately infer short-duration behaviors, identify repetitive micro-behaviors, and provide timely interventions related to oral hygiene. The dissertation\u27s contributions extend beyond the development of these models. Firstly, precise and detailed labels for each brief and micro-repetitive behavior are acquired to train and validate the models effectively. This involved meticulous marking of the exact start and end times of each event, including any intervening pauses, at a second-level granularity. A comprehensive scientific research study was conducted to collect such data from participants in their free-living natural environments. Secondly, a solution is proposed to address the issue of sensor placement variability. Given the different positions of the sensor within a wristband and variations in wristband placement on the wrist, the model needs to determine the relative configuration of the inertial sensor accurately. Accurately determining the relative positioning of the inertial sensor with respect to the wrist is crucial for the model to determine the orientation of the hand. Additionally, time synchronization errors between sensor data and associated video, despite both being collected on the same smartphone, are addressed through the development of an algorithm that tightly synchronizes the two data sources without relying on an explicit anchor event. Furthermore, an event-based approach is introduced to identify candidate segments of data for applying machine learning models, outperforming the traditional fixed window-based approach. These candidate segments enable reliable detection of brief daily behaviors in a computationally efficient manner suitable for real-time. The dissertation also presents a computationally lightweight method for identifying anchor events using wrist-worn inertial sensors. Anchor events play a vital role in assigning unambiguous labels in a fixed-length window-based approach to data segmentation and effectively demarcating transitions between micro-repetitive events. Significant features are extracted, and explainable machine learning models are developed to ensure reliable detection of brief daily and micro-repetitive behaviors. Lastly, the dissertation addresses the crucial factor of the opportune moment for intervention during brief daily behaviors using wrist-worn inertial sensors. By leveraging these sensors, users can receive timely and personalized interventions to enhance their performance and improve their lifestyles. Overall, this dissertation makes substantial contributions to the field of real-time monitoring of short-duration behaviors. It tackles various technical challenges, provides innovative solutions, and demonstrates the potential for wrist-worn sensors to facilitate effective interventions and promote healthier behaviors. By advancing our understanding of these behaviors and optimizing intervention strategies, this research has the potential to significantly impact individuals\u27 well-being and contribute to the development of personalized health solutions

Reconhecimento de Exercícios Físicos em Tempo-Real em Dispositivos Wearable

Os grandes avanços tecnológicos têm permitido o desenvolvimento de novos equipamentos móveis com elevadas capacidades e que permitem uma utilização não intrusiva e ubíqua com os utilizadores. Os dispositivos wearable, têm sido apresentados por diversos fabricantes e tornam-se cada vez mais omnipresentes. A utilização de sensores permite a monitorização de dados externos e o desenvolvimento de aplicações inteligentes que têm como objetivo facilitar a vida dos utilizadores ou fornecer apoio adicional em áreas tão diversas como a saúde, desporto, ambientes de vida assistida, etc. No âmbito desta dissertação, desenvolveu-se uma aplicação a ser utilizada num dispositivo wearable e que pode ser vista como um personal trainer que valida um conjunto de exercícios propostos numa sessão de desporto. A solução desenvolvida, usa os sensores inerciais de um smartwatch Android Wear, para com base num conjunto de algoritmos de reconhecimento de padrões, detetar a taxa de sucesso na execução de um treino planeado. O facto de todo o processamento ser realizado no próprio dispositivo, é um fator diferenciador para outras soluções existentes.Technological advances have allowed the development of new mobile devices with high capacity and allow non-intrusive and ubiquitous use. Wearable devices have been introduced in the market by several manufacturers and are becoming increasingly ubiquitous. Sensors allows monitoring external data and developing intelligent applications that aim to make life easier for users and may provide additional support in areas as diverse as health, sports, ambient assisted living, etc. As part of this work, an application to be used in a wearable device and that can be seen as a personal trainer which validates a set of exercises proposed in a sport session, was developed. The developed solution uses inertial sensors of an Android Wear smartwatch, and based on a set of pattern recognition algorithms, detects the rate of success in the execution of a lanned workout. The fact that all processing can be performed on the device itself, is a differentiator factor to other existing solutions

Using brain-computer interaction and multimodal virtual-reality for augmenting stroke neurorehabilitation

Every year millions of people suffer from stroke resulting to initial paralysis, slow motor recovery and chronic conditions that require continuous reha bilitation and therapy. The increasing socio-economical and psychological impact of stroke makes it necessary to find new approaches to minimize its sequels, as well as novel tools for effective, low cost and personalized reha bilitation. The integration of current ICT approaches and Virtual Reality (VR) training (based on exercise therapies) has shown significant improve ments. Moreover, recent studies have shown that through mental practice and neurofeedback the task performance is improved. To date, detailed in formation on which neurofeedback strategies lead to successful functional recovery is not available while very little is known about how to optimally utilize neurofeedback paradigms in stroke rehabilitation. Based on the cur rent limitations, the target of this project is to investigate and develop a novel upper-limb rehabilitation system with the use of novel ICT technolo gies including Brain-Computer Interfaces (BCI’s), and VR systems. Here, through a set of studies, we illustrate the design of the RehabNet frame work and its focus on integrative motor and cognitive therapy based on VR scenarios. Moreover, we broadened the inclusion criteria for low mobility pa tients, through the development of neurofeedback tools with the utilization of Brain-Computer Interfaces while investigating the effects of a brain-to-VR interaction.Todos os anos, milho˜es de pessoas sofrem de AVC, resultando em paral isia inicial, recupera¸ca˜o motora lenta e condic¸˜oes cr´onicas que requerem re abilita¸ca˜o e terapia cont´ınuas. O impacto socioecon´omico e psicol´ogico do AVC torna premente encontrar novas abordagens para minimizar as seque las decorrentes, bem como desenvolver ferramentas de reabilita¸ca˜o, efetivas, de baixo custo e personalizadas. A integra¸c˜ao das atuais abordagens das Tecnologias da Informa¸ca˜o e da Comunica¸ca˜o (TIC) e treino com Realidade Virtual (RV), com base em terapias por exerc´ıcios, tem mostrado melhorias significativas. Estudos recentes mostram, ainda, que a performance nas tare fas ´e melhorada atrav´es da pra´tica mental e do neurofeedback. At´e a` data, na˜o existem informac¸˜oes detalhadas sobre quais as estrat´egias de neurofeed back que levam a uma recupera¸ca˜o funcional bem-sucedida. De igual modo, pouco se sabe acerca de como utilizar, de forma otimizada, o paradigma de neurofeedback na recupera¸c˜ao de AVC. Face a tal, o objetivo deste projeto ´e investigar e desenvolver um novo sistema de reabilita¸ca˜o de membros supe riores, recorrendo ao uso de novas TIC, incluindo sistemas como a Interface C´erebro-Computador (ICC) e RV. Atrav´es de um conjunto de estudos, ilus tramos o design do framework RehabNet e o seu foco numa terapia motora e cognitiva, integrativa, baseada em cen´arios de RV. Adicionalmente, ampli amos os crit´erios de inclus˜ao para pacientes com baixa mobilidade, atrav´es do desenvolvimento de ferramentas de neurofeedback com a utilizac¸˜ao de ICC, ao mesmo que investigando os efeitos de uma interac¸˜ao c´erebro-para-RV

Wearable and IoT technologies application for physical rehabilitation

This research consists in the development an IoT Physical Rehabilitation solution based on wearable devices, combining a set of smart gloves and smart headband for use in natural interactions with a set of VR therapeutic serious games developed on the Unity 3D gaming platform. The system permits to perform training sessions for hands and fingers motor rehabilitation. Data acquisition is performed by Arduino Nano Microcontroller computation platform with ADC connected to the analog measurement channels materialized by piezo-resistive force sensors and connected to an IMU module via I2C. Data communication is performed using the Bluetooth wireless communication protocol. The smart headband, designed to be used as a first- person-controller in game scenes, will be responsible for collecting the patient's head rotation value, this parameter will be used as the player's avatar head rotation value, approaching the user and the virtual environment in a semi-immersive way. The acquired data are stored and processed on a remote server, which will help the physiotherapist to evaluate the patients' performance around the different physical activities during a rehabilitation session, using a Mobile Application developed for the configuration of games and visualization of results. The use of serious games allows a patient with motor impairments to perform exercises in a highly interactive and non-intrusive way, based on different scenarios of Virtual Reality, contributing to increase the motivation during the rehabilitation process. The system allows to perform an unlimited number of training sessions, making possible to visualize historical values and compare the results of the different performed sessions, for objective evolution of rehabilitation outcome. Some metrics associated with upper limb exercises were also considered to characterize the patient’s movement during the session.Este trabalho de pesquisa consiste no desenvolvimento de uma solução de Reabilitação Física IoT baseada em dispositivos de vestuário, combinando um conjunto de luvas inteligentes e uma fita-de-cabeça inteligente para utilização em interações naturais com um conjunto de jogos terapêuticos sérios de Realidade Virtual desenvolvidos na plataforma de jogos Unity 3D. O sistema permite realizar sessões de treino para reabilitação motora de mãos e dedos. A aquisição de dados é realizada pela plataforma de computação Arduino utilizando um Microcontrolador Nano com ADC (Conversor Analógico-Digital) conectado aos canais de medição analógicos materializados por sensores de força piezo-resistivos e a um módulo IMU por I2C. A comunicação de dados é realizada usando o protocolo de comunicação sem fio Bluetooth. A fita-de-cabeça inteligente, projetada para ser usada como controlador de primeira pessoa nos cenários de jogo, será responsável por coletar o valor de rotação da cabeça do paciente, esse parâmetro será usado como valor de rotação da cabeça do avatar do jogador, aproximando o utilizador e o ambiente virtual de forma semi-imersiva. Os dados adquiridos são armazenados e processados num servidor remoto, o que ajudará o fisioterapeuta a avaliar o desempenho dos pacientes em diferentes atividades físicas durante uma sessão de reabilitação, utilizando uma Aplicação Móvel desenvolvido para configuração de jogos e visualização de resultados. A utilização de jogos sérios permite que um paciente com deficiências motoras realize exercícios de forma altamente interativa e não intrusiva, com base em diferentes cenários de Realidade Virtual, contribuindo para aumentar a motivação durante o processo de reabilitação. O sistema permite realizar um número ilimitado de sessões de treinamento, possibilitando visualizar valores históricos e comparar os resultados das diferentes sessões realizadas, para a evolução objetiva do resultado da reabilitação. Algumas métricas associadas aos exercícios dos membros superiores também foram consideradas para caracterizar o movimento do paciente durante a sessão