Motion capture sensing techniques used in human upper limb motion: a review

Purpose Motion capture system (MoCap) has been used in measuring the human body segments in several applications including film special effects, health care, outer-space and under-water navigation systems, sea-water exploration pursuits, human machine interaction and learning software to help teachers of sign language. The purpose of this paper is to help the researchers to select specific MoCap system for various applications and the development of new algorithms related to upper limb motion. Design/methodology/approach This paper provides an overview of different sensors used in MoCap and techniques used for estimating human upper limb motion. Findings The existing MoCaps suffer from several issues depending on the type of MoCap used. These issues include drifting and placement of Inertial sensors, occlusion and jitters in Kinect, noise in electromyography signals and the requirement of a well-structured, calibrated environment and time-consuming task of placing markers in multiple camera systems. Originality/value This paper outlines the issues and challenges in MoCaps for measuring human upper limb motion and provides an overview on the techniques to overcome these issues and challenges

Remote sensing technologies for physiotherapy assessment

The paper presents a set of remote, unobtrusive sensing technologies that can be used in upper and lower limbs rehabilitation monitoring. The advantages of using sensors based on microwave Doppler radar or infrared technologies for physiotherapy assessment are discussed. These technologies allow motion sensing at distance from monitored subject, reducing thus the discomfort produced by some wearable technologies for limbs movement assessment. The microwave radar that may be easily hidden into environment by nonmetallic parts allows remote sensing of human motion, providing information on user movements characteristics and patterns. The infrared technologies - infrared LEDs from Leap-Motion, infrared laser from Kinect depth sensor, and infrared thermography can be used for different movements' parameters evaluation. Visible for users, Leap-motion and Kinect sensors assure higher accuracy on body parts movements' detection at low computation load. These technologies are commonly used for virtual reality (VR) and augmented reality (AR) scenarios, in which the user motion patterns and the muscular activity might be analyzed. Thermography can be employed to evaluate the muscular loading. Muscular activity during movements training in physiotherapy can be estimated through skin temperature measurement before and after physical training. Issues related to the considered remote sensing technologies such as VR serious game for motor rehabilitation, signal processing and experimental results associated with microwave radar, infrared sensors and thermography for physiotherapy sensing are included in the paper.info:eu-repo/semantics/acceptedVersio

Personalized ambient parameters monitoring: design and implementing of a wrist-worn prototype for hazardous gases and sound level detection

The concentration is on “3D space utilization” as the concept and infrastructure of designing of a wearable in ambient parameters monitoring. This strategy is implemented according to “multi-layer” approach. In this approach, each group of parameters from the same category is monitored by a modular physical layer enriched with the respected sensors. Depending on the number of parameters and layers, each physical layer is located on top of another. The intention is to implement a device for “everyone in everywhere for everything”

IoPhyR - physical rehabilitation IoT system: sistema de reabilitação motora baseado em andarilhos inteligentes e IoT

A presente dissertação descreve o desenvolvimento de um sistema IoT (Internet of Things) de reabilitação física baseado em smart walkers. O sistema inclui sensores de tipo IMU (Inertial Measurement Unit), sensores de força (células de carga), e sensores de proximidade (sensores de ultrassons). Os sinais adquiridos por uma plataforma de computação com microcontrolador ligada aos sensores permitem calcular métricas associadas a caracterização da orientação e do equilíbrio do paciente, assim como elementos relacionados com a utilização do andarilho como a elevação do mesmo, o número de passos efetuados e a força exercida sobre os pés do andarilho. O smart walker utiliza a plataforma de computação de tipo Arduino Mega para a realização do cálculo de métricas ligadas a caracterização das sessões de fisioterapia que serão posteriormente armazenadas na nuvem. Os dados adquiridos ao nível dos smart walkers são transmitidos para a nuvem do sistema, utilizando um módulo Wi-Fi, ou por intermédio de um tablet que recebe os dados da sessão em curso através de comunicação Bluetooth, sendo realizada uma sincronização de dados tablet-nuvem.A análise e visualização dos dados armazenados é realizada através da webapp e aplicação móvel desenvolvidas.This dissertation describes the development of an Arduino based physical rehabilitation IoT system. The system uses metrics acquired from IMU sensors (Inertial Measurement Unit), pressure sensors (load cells) and distance sensors (ultrasound sensors). The metrics extracted from these sensors help to determine the patient’s orientation, the number of steps taken, and the patient’s balance. The smart walker uses the Arduino Mega platform to calculate the required metrics during the physiotherapy sessions to store them in the system cloud server afterwards. The cloud server storing process is done straight from the smart walker, using a Wi-Fi module, or through a mobile device with the system’s mobile app, using a Bluetooth module. The stored data analysis and visualization is performed through the developed system’s user interfaces (a webapp and a mobile app)

Investigation of novel control strategies for promoting motor learning in the upper limb with a haptic computer exercise system in able-bodied adults and those with motor impairments

Motor impairments caused by stroke and cerebral palsy (CP) are common and often affect the function of the upper limb, which to be restored requires rehabilitation. As positive outcome is correlated to how early and intensive therapy is and since the resources of the healthcare providers are limited, robotic devices have been introduced to provide adjunctive therapy. The algorithms that control the manner those devices apply forces to the impaired limb are called haptic control algorithms (HCA) and to this date there has not been conclusive evidence as to what the behaviour of these algorithms should be. One type of HCAs is error augmentation (EA) which is a rather understudied but promising approach. This work presents to the literature two novel control strategies of the EA type that incorporate adaptive features namely Error Augmenting Adaptive(EA) and Error Augmenting Proportional (EA). Those two algorithms were implemented for and deployed to a single point of attachment robotic rehabilitation system. The effectiveness in inducing motor learning of the developed algorithms was evaluated in a trial with able-bodied participants and compared against a third more established assistive HCA namely Assistance As Needed (AAN) and a control condition (no forces). Four groups (one per condition) practised reaching movements with a speed and accuracy requirement using their non-dominant arm to interact with the robot under a visual rotation of a 100o. To assess learning kinematic measures were collected to measure their performance on reaching and circle-drawing movements. Also, bilateral transfer to the arm that did not receive practice was assessed. Changes in the participants’ valence, arousal and dominance were assessed with a Self-Assessment Manikin questionnaire. All groups learned to move their non-dominant arm under a visual perturbation showing comparable improvements in all key measures (p<0.05). Passive movements and EAP led to greater improvement in movement smoothness (p<0.05) and resulted in more retention of the improvements after a washout block (p<0.05) was introduced. Conversely, EAA showed a better effect on improving mean velocity (p<0.05). All groups performed similarly in terms of improving movement error and duration but EAA and AAN achieved peak performance faster (p<0.05). Similar improvements were measured on the arm that did not receive any training which were fully retained post-washout indicating that bilateral transfer occurred and led to better retention (p<0.05). The findings of this work indicate that different attributes can be exploited from the developed HCAs to induce motor learning and improve different aspects of the movement suggesting that multimodal training protocols tailored to the needs of the patient are the way forward. Also, this work showed that bilateral transfer training has great potential in upper limb rehabilitation and the positive effects of the different HCAs on the arm that received practice transfer to the one that did not receive training. It is recommended that the findings of this work to be further investigated in experimental therapy protocols for those who suffer from neurological impairments such stroke and CP