A proposed adaptive soft orthotic device performs motion sensing and production of assistive forces with a modular, pneumatically-driven, hyper-elastic composite. Wrapping the material around a joint will allow simultaneous motion sensing and active force response through shape and rigidity control. This monolithic elastomer sheet contains a series of miniaturized pneumatically-powered McKibben-type actuators that exert tension and enable adaptive rigidity control. The elastomer is embedded with conductive liquid channels that detect strain and bending deformations induced by the pneumatic actuators. In addition, the proposed system is modular and can be configured for a diverse range of motor tasks, joints, and human subjects. This modular functionality is accomplished with a decentralized network of self-configuring nodes that manage the collection of sensory data and the delivery of actuator feedback commands. This paper mainly describes the design of the soft orthotic device as well as actuator and sensor components. The characterization of the individual sensors, actuators, and the integrated device is also presented.

We describe the design of an active soft ankle-foot orthotic device powered by pneumatic artificial muscles for treating gait pathologies associated with neuromuscular disorders. The design is inspired by the biological musculoskeletal system of a human foot and a lower leg, and mimics the muscle-tendon-ligament structure. A key feature of the device is that it is fabricated with flexible and soft materials that provide assistance without restricting degrees of freedom at the ankle joint. Three pneumatic artificial muscles assist dorsiflexion as well as inversion and eversion. The prototype is also equipped with various embedded sensors for gait training and gait pattern analysis. The prototype is capable of 12° dorsiflexion from a resting position of an ankle joint and a 20° dorsiflexion from plantarflexion. Results of early feedback control experiments show controllability of ankle joint angles. Ultimately, we envision a system that not only can provide physical support to improve mobility but also can increase safety and stability during walking, while enhancing muscle usage and encouraging rehabilitation.

We present MercuryLive, a web-enhanced extension to a body sensor network platform for continuous home-based body motion sensing, interactive supervised data collection sessions, and long-term activity data analysis. The major goal of MercuryLive is to enable practical long-term health monitoring in a home setting and henceforth reduce the effort and cost for collecting clinically relevant quantitative measures on patients' health conditions during daily activities. MercuryLive contains three tiers: a central web server for streaming and storage of sensor data, a sensor data collection engine, and a user-friendly web-based GUI client. The platform is currently used in clinical studies on Parkinson's disease.

This paper describes Mercury, a wearable, wireless sensor platform for motion analysis of patients being treated for neuromotor disorders, such as Parkinson's Disease, epilepsy, and stroke. In contrast to previous systems intended for short-term use in a laboratory, Mercury is designed to support long-term, longitudinal data collection on patients in hospital and home settings. Patients wear up to 8 wireless nodes equipped with sensors for monitoring movement and physiological conditions. Individual nodes compute high-level features from the raw signals, and a base station performs data collection and tunes sensor node parameters based on energy availability, radio link quality, and application specific policies.

This paper presents Pixie, a new sensor node operating system designed to support the needs of data-intensive applications. These applications, which include high-resolution monitoring of acoustic, seismic, acceleration, and other signals, involve high data rates and extensive in-network processing. Given the fundamentally resource-limited nature of sensor networks, a pressing concern for such applications is their ability to receive feedback on, and adapt their behavior to, fluctuations in both resource availability and load.

No abstract available.

Developing sensor network applications demands a new set of tools to aid programmers. A number of simulation environments have been developed that provide varying degrees of scalability, realism, and detail for understanding the behavior of sensor networks. To date, however, none of these tools have addressed one of the most important aspects of sensor application design: that of power consumption. While simple approximations of overall power usage can be derived from estimates of node duty cycle and communication rates, these techniques often fail to capture the detailed, low-level energy requirements of the CPU, radio, sensors, and other peripherals.