Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation

BACKGROUND: Monitoring body kinematics has fundamental relevance in several biological and technical disciplines. In particular the possibility to exactly know the posture may furnish a main aid in rehabilitation topics. In the present work an innovative and unobtrusive garment able to detect the posture and the movement of the upper limb has been introduced, with particular care to its application in post stroke rehabilitation field by describing the integration of the prototype in a healthcare service. METHODS: This paper deals with the design, the development and implementation of a sensing garment, from the characterization of innovative comfortable and diffuse sensors we used to the methodologies employed to gather information on the posture and movement which derive from the entire garments. Several new algorithms devoted to the signal acquisition, the treatment and posture and gesture reconstruction are introduced and tested. RESULTS: Data obtained by means of the sensing garment are analyzed and compared with the ones recorded using a traditional movement tracking system. CONCLUSION: The main results treated in this work are summarized and remarked. The system was compared with a commercial movement tracking system (a set of electrogoniometers) and it performed the same accuracy in detecting upper limb postures and movements

Wearable Kinesthetic System in Post-stroke Rehabilitation: A Review of Sensor in Body Motions Detection

This paper presents a system with various kinematics parameters considered to capture and classify body gestures for user’s recovery. The concepts involved are briefly explained in this paper. Basically, two devices concepts are explained, which are the Upper Limb Kinesthetic Garment (ULKG) and OPAL technologies. The method of literature search used is discussed in methodology, while detailed information from reviews on particular devices is analysed. Then, the performance and feedback from users are compiled to indicate usability on both devices under the results section. Both ULKG that used conductive elastomer (CE) and OPAL sensor are compared to figure out which sensor is more appropriate for users

Wearable Kinesthetic System In Post-Stroke Rehabilitation: A Review Of Sensor In Body Motions Detection

This paper presents a system with various kinematics parameters considered to capture and classify body gestures for user’s recovery. The concepts involved are briefly explained in this paper. Basically, two devices concepts are explained, which are the Upper Limb Kinesthetic Garment (ULKG) and OPAL technologies. The method of literature search used is discussed in methodology, while detailed information from reviews on particular devices is analysed. Then, the performance and feedback from users are compiled to indicate usability on both devices under the results section. Both ULKG that used conductive elastomer (CE) and OPAL sensor are compared to figure out which sensor is more appropriate for users

Advances in wearable technology and applications in physical medicine and rehabilitation

The development of miniature sensors that can be unobtrusively attached to the body or can be part of clothing items, such as sensing elements embedded in the fabric of garments, have opened countless possibilities of monitoring patients in the field over extended periods of time. This is of particular relevance to the practice of physical medicine and rehabilitation. Wearable technology addresses a major question in the management of patients undergoing rehabilitation, i.e. have clinical interventions a significant impact on the real life of patients? Wearable technology allows clinicians to gather data where it matters the most to answer this question, i.e. the home and community settings. Direct observations concerning the impact of clinical interventions on mobility, level of independence, and quality of life can be performed by means of wearable systems. Researchers have focused on three main areas of work to develop tools of clinical interest: 1)the design and implementation of sensors that are minimally obtrusive and reliably record movement or physiological signals, 2)the development of systems that unobtrusively gather data from multiple wearable sensors and deliver this information to clinicians in the way that is most appropriate for each application, and 3)the design and implementation of algorithms to extract clinically relevant information from data recorded using wearable technology. Journal of NeuroEngineering and Rehabilitation has devoted a series of articles to this topic with the objective of offering a description of the state of the art in this research field and pointing to emerging applications that are relevant to the clinical practice in physical medicine and rehabilitation

Markerless assisted rehabilitation system

The project focuses on the use of modern technology to analyze human movement. This analysis turns out to be useful aid for physicians in rehabilitation of patients with limb injuries. This method is more precise than simple observation of the patient through the organ of sight. The proposed system allows markerless determination of deviations between the selected bones and joints, and as a result do not require specialized and expensive equipment. The implemented application presents instructional animation of the exercises and verify the correctness of its performance in real time. The equipment that meets the requirements of the project is the Microsoft Kinect, which is nowadays widely used in the medical field

Optimal Accelerometer Placement for Fall Detection of Rehabilitation Patients

The development of health monitoring system using wearable sensor has lots of potential in the field of rehabilitation and gained lots of attention in the scientific community and industry. The aim and motivation in this field are to focus on the application of wearable technology to monitor elderly or rehab patients in home-based settings to reduce resources and development cost. The wearable sensor such as accelerometer used to emphasise the clinical applications of fall detection during rehabilitation treatment. This paper is intended to determine the optimal sensor placement especially for lower limb activity during rehabilitation exercise. Accelerometer data were collected from three different body locations (hip, thigh, and foot). The lower limb activities involve normal movements such as walking, lifting, sit-to-stand, and stairs. Other unexpected activity such as falls might occur during normal lower limb exercise movement. Then, acceleration data for various lower limbs activities was classified using k-NN and SVM classifier. The result found that the hip was the best location to record data for lower limb activities including when fall occurs

Automatic identification of gait events using an instrumented sock

Background: textile-based transducers are an emerging technology in which piezo-resistive properties of materials are used to measure an applied strain. By incorporating these sensors into a sock, this technology offers the potential to detect critical events during the stance phase of the gait cycle. This could prove useful in several applications, such as functional electrical stimulation (FES) systems to assist gait. Methods: we investigated the output of a knitted resistive strain sensor during walking and sought to determine the degree of similarity between the sensor output and the ankle angle in the sagittal plane. In addition, we investigated whether it would be possible to predict three key gait events, heel strike, heel lift and toe off, with a relatively straight-forward algorithm. This worked by predicting gait events to occur at fixed time offsets from specific peaks in the sensor signal. Results: our results showed that, for all subjects, the sensor output exhibited the same general characteristics as the ankle joint angle. However, there were large between-subjects differences in the degree of similarity between the two curves. Despite this variability, it was possible to accurately predict gait events using a simple algorithm. This algorithm displayed high levels of trial-to-trial repeatability. Conclusions: this study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance

An enhanced power harvesting from woven textile using piezoelectric materials

The field of power harvesting has experienced significant growth over the past few years due to the ever-increasing desire to produce portable and wireless electronics with extended lifespans. The present work aims to introduce an approach to harvesting electrical energy from a mechanically excited piezoelectric element and investigates a power analytical model generated by a smart structure of type polyvinylidene fluoride(PVDF) that can be stuck onto fabrics and flexible substrates. Moreover, we report the effects of various substrates and investigates the sticking of these substrates on the characterization of the piezoelectric material