Performance analysis and biomechanical power estimation using a novel lower-limb wearable sensing system

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Wearable technology is experiencing very rapid growth in terms of new applications being explored and the number of wearable devices sold in the market. Applications range from virtual reality to health and fitness. Activity, fitness, and athletics-related devices are one of the most dominant categories of wearable devices. Based on academic research conducted over a number of years, Gesturelogic (GL) is commercializing the LEO wearable system for health and fitness activities with a focus on lower limb and initial application in biking. An investment raise of approximately $1.9M in 2014 from OCE and angel investors has provided GL with the working capital to bring LEO to market as quickly as possible. LEO features an integrated sensing and processing system used in the form of a thigh band. It can track both motion and muscle activity using surface electromyographyic (EMG)signals. Given its unique form factor it can be worn on the thigh, thus enabling a novel sensor for walking, running, cycling, and generally any activity utilizing lower limbs. This project initiates a collaborative investigation between Carleton University's Advanced Biomechatronics Laboratory and Gesturelogic Inc (GL) to conduct two research modules that are important in validating and enhancing the LEO system: (1) Sensor performance analysis funded by an OCE-VIP grant This project consists of an experimental study to better understand LEO's novel EMG sensor, develop custom real-time and post-processing techniques to improve signal quality, and investigate the impact of varying environmental and biomechanical conditions on sensor performance. Data will be collected and analyzed to make recommendation for improvements on the product and the embedded processing techniques. (2) Biomechcanical power estimation funded by an NSERC Engage grant GL is interested in exploring the potential to use a single wearable device, at the thigh with possible inertial and EMG sensing capabilities, to estimate the users biomechanical power output while biking. It is proposed to combine the measured accelerations and EMG signals from the thigh muscles together with simple kinematics and dynamics (or inverse kinematics and dynamics) models of the human legs and pedaling system to come up with a real-time estimator for the output power. The research includes proposing appropriate algorithms to fuse information, define and conduct pilot studies, and experimentally validate the proposed methods. Successful execution of these research efforts will result in validation of GL's new sensing system, product modifications, and new product features, which together can strengthen LEO's already deep technology.