LPcomS: Towards a Low Power Wireless Smart-Shoe System for Gait Analysis in People with Disabilities

Gait analysis using smart sensor technology is an important medical diagnostic process and has many applications in rehabilitation, therapy and exercise training. In this thesis, we present a low power wireless smart-shoe system (LPcomS) to analyze different functional postures and characteristics of gait while walking. We have designed and implemented a smart-shoe with a Bluetooth communication module to unobtrusively collect data using smartphone in any environment. With the design of a shoe insole equipped with four pressure sensors, the foot pressure is been collected, and those data are used to obtain accurate gait pattern of a patient. With our proposed portable sensing system and effective low power communication algorithm, the smart-shoe system enables detailed gait analysis. Experimentation and verification is conducted on multiple subjects with different gait including free gait. The sensor outputs, with gait analysis acquired from the experiment, are presented in this thesis

smartPrediction: A Real-time Smartphone-based Fall Risk Prediction and Prevention System

The high risk of falls and the substantial increase in the elderly population have recently stimulated scientific research on Smartphone-based fall detection systems. Even though these systems are helpful for fall detection, the best way to reduce the number of falls and their consequences is to predict and prevent them from happening in the first place. To address the issue of fall prevention, in this paper, we propose a fall prediction system by integrating the sensor data of Smartphones and a Smartshoe. We designed and implemented a Smartshoe that contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data in any environment. By assimilating the Smartshoe and Smartphone sensors data, we performed an extensive set of experiments to evaluate normal and abnormal walking patterns. The system can generate an alert message in the Smartphone to warn the user about the high-risk gait patterns and potentially save them from an imminent fall. We validated our approach using a decision tree with 10-fold cross validation and found 97.2% accuracy in gait abnormality detection

iPrevention: Towards a Novel Real-Time Smartphone-Based Fall Prevention System

Falling remains one of the leading causes of hospitalization and death for the elderly all around the world. The considerable risk of falls and the substantial increase of the elderly population have stimulated scientific research on smartphone-based fall detection systems recently. Even though these systems are helpful for fall detection, the best way to reduce the number of falls and their consequences is to prevent them from happening in the first place. Therefore, our focus is on fall prevention rather than fall detection. To address the issue of fall prevention, in this paper, we propose a smartphone-based fall prevention system that can alert the user about their abnormal walking pattern. Most current systems merely detect a fall whereas our approach attempts to identify high-risk gait patterns and alert the user to save them from an imminent fall. Our system uses a gait analysis approach that couples cycle detection with feature extraction to detect gait abnormality. We validated our approach using a decision tree with 10-fold cross validation and found 99.8% accuracy in gait abnormality detection. To the best of our knowledge, we are the first to use the built-in accelerometer and gyroscope of the smartphone to identify abnormal gaits in users for fall prevention