Wearable feedback systems for rehabilitation

Abstract

In this paper we describe LiveNet, a flexible wearable platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification. Based on the MIT Wearable Computing Group's distributed mobile system architecture, LiveNet is a stable, accessible system that combines inexpensive, commodity hardware; a flexible sensor/peripheral interconnection bus; and a powerful, light-weight distributed sensing, classification, and inter-process communications software architecture to facilitate the development of distributed real-time multi-modal and context-aware applications. LiveNet is able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. We demonstrate the power and functionality of this platform by describing a number of health monitoring applications using the LiveNet system in a variety of clinical studies that are underway. Initial evaluations of these pilot experiments demonstrate the potential of using the LiveNet system for real-world applications in rehabilitation medicine.

Stress Detection Using Wearable Physiological and Sociometric Sensors

“…The LiveNet system presented in [76] is a complete wearable hardware and software system for long term monitoring which, according to the authors, has potential applications for monitoring soldiers and Parkinson's patients. It could also detetct epilepsy seizures.…”

Mobile, social, and wearable computing and the evolution of psychological practice.

“…Interdisciplinary fields such Ubiquitous Computing, Pervasive Computing, Persuasive Technology, Affective Technology, and Human Computer Interaction emerged to share and cultivate this research. The “sociometer” worn microphone that measured conversational turn-taking (Choudhury, 2003), signal processing of affect in voice and speech (e.g., Scherer, 2003; Sung, Marci, & Pentland, 2005), a steering wheel that senses stress from galvanic skin response (Healey & Picard, 2005), mobile therapy activated by a wireless electrocardiogram (Morris & Guilak, 2009), a photographic journal for diabetics (Frost & Smith, 2002), and Gordon Bell’s digital life chronicle (2001) are examples of early research projects that paved the way for today’s products and ongoing innovation.…”

Unobstructive Body Area Networks (BAN) for Efficient Movement Monitoring

“…LiveNet [9] allows people to receive real-time information about their health status and can also communicate with care givers. It also embeds a learning infrastructure based in statistical machine learning techniques in order to distinguish shivering from normal body movements.…”