Department Publications

There are several systems that use one or several Kinect sensors for human gait analysis, particularly for diagnosis of patients. However, due to the limited depth sensing range of the Kinect—a sensor manufactured for video gaming—the depth measurement accuracy reduces with distance from the Kinect. In addition, self-occlusion of the subject limits the accuracy and utility of such systems. We overcome these limitations by using a two-Kinect gait analysis system and mechanically moving the Kinects in synchronization with the test subject and each other. This increases the practical measurement range of the Kinect-based system whilst maintaining the measurement accuracy. Results of the comparison of knee flexion, step length, and stride length with a software based method show that our moving Kinect system can accurately analyse these gait parameters.

This paper presents a novel low-complexity VLSI architecture for an EEG Feature Extraction Platform for wearable health monitoring systems. It integrates a processing core unit, preprocessing FIR filter block and an FFT accelerator. The platform supports 8 scalp EEG channels and it can be scaled up to 16 or 32 EEG channels. In this paper, we analyze several architectural optimizations for hardware implementation of features such as Power Spectral Density, Discrete Wavelet Transform, Autoregression etc. The proposed architecture is easily scalable to new feature extractors. An FFT accelerator is proposed to improve the flexibility of the platform. We provide a comparison with software models for each feature extractor and also validate the platform by implementing a seizure detection algorithm on CHB-MIT Scalp EEG database with an accuracy of over 95%.

Human gait analysis, the study of human locomotion, is possible with low-cost RGBD sensors such as the Kinect sensor. However, due to the inherent depth sensing accuracy limitations of these sensors as the distance from the sensor increases, the distance range of gait analysis too becomes small and inefficient for clinical use. We present a system that uses two independent Kinects in a data fusion framework that increases the distance range of gait analysis from 2.5 m to 4 m with three gait cycles. Our gait parameters are reasonably accurate and comparable with existing systems with 4% error in length measurements and 5° error in flexion measurements. The system is extensible to have several Kinects.

Fitness related wearables have become ubiquitous in the recent past. Nevertheless, short battery life of these devices is still a pressing issue. Limited battery capacity in small form factor and power hungry continuous monitoring of accelerometer have been significant concerns in this regard. To address these issues we propose a novel low-power step counting solution based on an Electromagnetic energy harvesting mechanism. Extremely simple nature of the step counter removes the requirement of any step detection algorithm, thereby reducing the power consumption, while the energy harvester generates a portion of energy requirement prolonging the battery life.