A non-ZUPT gait reconstruction method for ankle sensors.

Monitoring lower body motion, especially gait pattern, using low cost Inertial Measurement Units on a daily basis is becoming critically important for the diagnosis and rehabilitation of neurological diseases. The current state of the art algorithm is to double integrate motion acceleration and compensate cumulative errors by resetting velocity signals to zero at the stance-phase of each stride. However, this method is only applicable for foot-mounted sensors.

A simple calibration for upper limb motion tracking and reconstruction.

This paper extends the work of inertial sensor based upper limb motion tracking by introducing a simple calibration method to automatically construct a global reference frame and estimate arm length. The method has effectively eliminated the requirement of manually aligning the sensors' local reference frames when multiple sensors are used to track the movements of the individual arm segments. The capacity of arm length estimation also makes it possible to reconstruct position trajectories of the elbow and the wrist joints in a reference frame with the shoulder joint as the origin.

Personalized Multilayer Daily Life Profiling Through Context Enabled Activity Classification and Motion Reconstruction: An Integrated System Approach.

Profiling the daily activity of a physically disabled person in the community would enable healthcare professionals to monitor the type, quantity, and quality of their patients' compliance with recommendations for exercise, fitness, and practice of skilled movements, as well as enable feedback about performance in real-world situations. Based on our early research in in-community activity profiling, we present in this paper an end-to-end system capable of reporting a patient's daily activity at multiple levels of granularity: 1) at the highest level, information on the location categories a patient is able to visit; 2) within each location category, information on the activities a patient is able to perform; and 3) at the lowest level, motion trajectory, visualization, and metrics computation of each activity. Our methodology is built upon a physical activity prescription model coupled with MEMS inertial sensors and mobile device kits that can be sent to a patient at home.

Integrated inertial sensors and mobile computing for real-time cycling performance guidance via pedaling profile classification.

Today, the bicycle is utilized as a daily commute tool, a physical rehabilitation asset, and sporting equipment, prompting studies into the biomechanics of cycling. Of the number of important parameters that affect cycling efficiency, the foot angle profile is one of the most important as it correlates directly with the effective force applied to the bike. However, there has been no compact and portable solution for measuring the foot angle and for providing the cyclist with real-time feedback due to a number of difficulties of the current tracking and sensing technologies and the myriad types of bikes available.

Context-driven, prescription-based personal activity classification: methodology, architecture, and end-to-end implementation.

Enabling large-scale monitoring and classification of a range of motion activities is of primary importance due to the need by healthcare and fitness professionals to monitor exercises for quality and compliance. Past work has not fully addressed the unique challenges that arise from scaling. This paper presents a novel end-to-end system solution to some of these challenges.