Wearables are a leading category in the Internet of Things. Compared with mainstream mobile phones, wearables target one order of magnitude form factor reduction, and offer the potential of providing ubiquitous, personalized services to end users. Aggressive reduction in size imposes serious limits on battery capacity. Wearables are equipped with a range of sensors, such as accelerometers and gyroscopes. Most economical sensors were developed for mobile phones, with energy consumptions more appropriate for phones than for ultra-compact wearables. This article describes the energy challenges for wearable sensing technologies, with a primary focus on the most widely used wearable sensors: MEMS-based inertial measurement units. Using sports and fitness wearables as the pilot application, we analyze the energy characteristics of MEMS IMU data sensing, analysis, and wireless communication. We then discuss the technologies needed to solve the power and energy consumptions challenges for wearables.

Data sensing and analysis: Challenges for wearables

Low-Power System Design for Human-Borne Sensing

Patients recovering from hip or knee arthroplasty tend to fall into pathological gait patterns partially caused by pain after the surgery, but also due to the new biomechanics of the joint. To assist in the rehabilitation of the patient to a normal gait, a portable system was developed which, based on the information from wireless wearable sensors, monitors the quality of the gait in real time and classifies the gait as normal or pathological. The system is built as a Wireless Body Area Network (WBAN) composed by a central unit with the algorithms implemented on an iPhone and one or more sensor units integrated into commercially available patellar tendon straps. The system provides an objective assessment of the gait quality to the patient, who is then able to autonomously correct their gait pattern and progressively achieve a complete rehabilitation.

A Novel Portable System for Gait Analysis and Rehabilitation.

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A Novel Portable System for Gait Analysis and Rehabilitation.