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. A novel context-guided activity-monitoring concept with categorical location context is used to achieve enhanced classification accuracy and throughput. The methodology is then seamlessly integrated with motion reconstruction and metrics computation to provide comprehensive layered reporting of a patient's daily life. We also present an implementation of the methodology featuring a novel location context detection algorithm using WiFi augmented GPS and overlays, with motion reconstruction and visualization algorithms for practical in-community deployment. Finally, we use a series of experimental field evaluations to confirm the accuracy of the system.

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. This paper presents a novel sensing and mobile computing system for classifying the foot angle profiles during cycling and for providing real-time guidance to the user to achieve the correct profile. Continuous foot angle tracking is firstly converted into a discrete problem requiring only recognition of acceleration profiles of the foot using a single shoe mounted tri-axial accelerometer during each pedaling cycle. A classification method is then applied to identify the pedaling profile. Finally, a mobile solution is presented to provide real-time signal processing and guidance.

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. The system is built on the prescription-based context-driven activity classification methodology. First, we show that by refining the definition of context, and introducing the concept of scenarios, a prescription model can provide personalized activity monitoring. Second, through a flexible architecture constructed from interface models, we demonstrate the concept of a context-driven classifier. Context classification is achieved through a classification committee approach, and activity classification follows by means of context specific activity models. Then, the architecture is implemented in an end-to-end system featuring an Android application running on a mobile device, and a number of classifiers as core classification components. Finally, we use a series of experimental field evaluations to confirm the expected benefits of the proposed system in terms of classification accuracy, rate, and sensor operating life.

Enabling large-scale ground-truth acquisition and system evaluation in wireless health.

Large-scale activity monitoring is a core component of systems aiming to improve our ability to manage fitness, deliver care, and diagnose conditions. While much research has been devoted to the accurate classification of motion, the challenges arising from scaling to large communities have received little attention. This paper introduces the problem of scaling, and addresses two of the most important issues: enabling robust large-scale ground-truth acquisition and building a common database for systems comparison. This paper presents a voice powered mobile acquisition system with efficient annotation tools and an extendable online searchable activity database with 331 datasets totaling over 700 h with 8 sensing modalities and 15 activities.