Abstract
Non-contact sensors are negating the use of wearables or cameras and providing a rewarding and accepting environment to assist in biomedical applications, such as physiological examinations, physiotherapy, home assistance, rehabilitation success determination, compliance, and health diagnostics. In this paper, physiological parameter identification of human gait has been demonstrated through an edge-based sensor and a heuristic approach. Impulse radio ultra-wide band (IR-UWB) pulsed Doppler radar has been employed with a focus on understanding human walking patterns. This paper extracts an individual’s gait trait from the associated biomechanical activity and differentiates lower limb movement patterns from other body areas via a radar transceiver. It is observed that Doppler shifts alone are not reliable to detect human gait because of frequency shifts taking place across the entire body (including breathing, heartbeat, and arm movements) where movement occurs. Thus, a heuristic spherical trigonometrical approach has been proposed to augment radar principles and short-term Fourier transformation (STFT) to identify the gait trait. The experiment presented includes data gathering from a number of male and female participants in both ideal and real environments. Subsequently, the proposed gait identification and parameter characterization has been analyzed, tested, and validated against popularly accepted smartphone applications where the variations are less than 5%.
Highlights
H UMAN gait is a complex mechanism [1] where different muscles coordinate to create human locomotion
This paper presents the first ever description and experimental demonstration of a non-contact pulsed UWB sensor system to identify and extract human gait from other simultaneous bio-mechanic actions such as, arm swing, breathing and heart rates
A UWB radar sensor with in-house developed algorithms is used for data collection and processing
Summary
H UMAN gait is a complex mechanism [1] where different muscles coordinate to create human locomotion. Other forms of NWS gait analysis examples are conducted with motion capture sensors [13] and force plates [14] in controlled laboratory observation settings These systems are effective to determine foot pressure, but unable to measure the components of that pressure [15] and can require a large amount of pressure to be applied for activation, may be unsuitable for elderly or weaker patients. Force Sensitive Resistors (FSR) only produce event detection information or contact timing [16], [17], [18] between the leg and ground which is significant characteristic in pathological gait analysis [19], it does not provide kinematic or spatial swing phase information This type of NWS systems is limited by immobility, price, and operational cost issues. It adds the overhead complexity and reduces the real-time reading opportunities
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