Computer Vision-Based Gait Velocity from Non-Obtrusive Thermal Vision Sensors

Abstract

Gait velocity is an important measure of independence and functional ability to those within the older population. Detecting changes in gait velocity can aid to provide interventions to avoid hospitalisation, currently gait velocity is assessed in a clinical setting, where the patient is timed over a measured distance between 3-6 metres by a clinician, however, this is time consuming, subjective, and not possible to carry out frequently over time. An unobtrusive method of monitoring gait velocity, frequently, over extended periods of time, would therefore be advantageous when developing interventions. This paper proposes an unobtrusive computer vision-based method of continuously monitoring an occupants gait velocity within their own home. This is achieved through the use of a low cost thermal vision sensor. The system was benchmarked against the clinical standard method of being timed by a stopwatch. Results show a high correlation between the gait velocity measured by the thermal vision sensor and the measured stopwatch velocity (R=0.941, p=0.02).

Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors

“…An effective factory calibration is integrated in the device, with no distortion by the fish-eye lens. 38 The data are collected from the TVS by means of a twisted Ethernet cable which is connected to the local area network. The middleware SensorCentral 39 integrates the TVS as a sensor source, providing the thermal sensor data within a Web Service in JSON format.…”

Video Based Mobility Monitoring of Elderly People Using Deep Learning Models

“…In [25], a Kinect camera and a neural network approach have been applied to identify the most significant gait characteristics and thus detect the disorders caused by Parkinson's disease. In [26], a low-cost thermal vision sensor has been proposed to continuously monitor the gait velocity of older people in their homes, showing a high correlation with that measured with a stopwatch. Two smartphone cameras have been used in [27] to classify normal and abnormal gait.…”

Human Gait Analysis in Neurodegenerative Diseases: A Review

“…Actually, the literature on the use of visionbased systems for the instrumented gait analysis of patients with neurodegenerative diseases counts few works compared to those based on wearable sensors or floor sensors. However, in the last few years, the progress in new and low-cost optical technologies together with the development of new and accurate pattern recognition approaches has led to an increase in vision-based research works [70], [71], [72], [73], [74], [75].…”