We propose two optimization techniques to minimize memory usage and computation while meeting system timing constraints for real-time classification in wearable systems. Our method derives a hierarchical classifier structure for Support Vector Machine (SVM) in order to reduce the amount of computations, based on the probability distribution of output classes occurrences. Also, we propose a memory optimization technique based on SVM parameters, which results in storing fewer support vectors and as a result requiring less memory. To demonstrate the efficiency of our proposed techniques, we performed an activity recognition experiment and were able to save up to 35% and 56% in memory storage when classifying 14 and 6 different activities, respectively. In addition, we demonstrated that there is a trade-off between accuracy of classification and memory savings, which can be controlled based on application requirements.

With our approach, for the synthesis and energy-efficient operation of a pressure sensor-based soft keyboard, energy is optimized at five levels of abstraction: 1) architecture of sensor systems, 2) sensing schedule, 3) sensor data processing, 4) use of semantic information, and 5) customization. We have been able to achieve up to a factor of 63 energy reduction in a keyboard case study over the standard using various techniques. For example, when each sensor senses several keyboard events and each event is sensed by three sensors, sensing and data processing results in a 12- to 42-fold energy savings depending on typing speeds. Customization techniques accounting for user typing speed and duration of typing pulses support another 1.5-fold savings. We demonstrate using a proof of concept prototype evaluation with five participants.

Recently Electronic Textile (E-Textile) technology enables the weaving computation and communication components into the clothes that we wear and objects that we interact with every day. E-Textile enables the design and development of a broad range of pervasive sensing systems such as smart bed sheets for sleep posture monitoring, insoles in medical shoes for monitoring plantar pressure distribution, smart garments and sensing gloves. However the low cost of E-Textiles come at the cost of accuracy. In this work we propose an actuator-based method that increases the accuracy of E-Textiles by means of enabling real-time calibration. Our proposed system increases system accuracy by 38.55% on average (maximum 58.4%).

The Wolf Motor Function Test (WMFT), which involves real time timing measurements, is used to define the functional ability of the paretic limb of post stroke patients. The accuracy and consistency of the timing results are crucial for scoring, while manual stopwatch measuring introduces mistakes and uncertainty. A technique which uses camera and sensors to automate the test and achieve accurate results is proposed here. This system measures time for seven tasks with a monitor camera and a Xilinx Virtex II Pro Field Programmable Gate Array (FPGA). The FPGA was mainly chosen for its better performance compared to general purpose System on Chip (SoC) for computational intensive tasks, such as image processing in this system. The accuracy of the system has been validated in a user study on selected WMFT tasks with ground truth time measurement obtained from pressure sensors. Suggestions are also given on how the framework can be adapted to the remaining tasks.

Determining who is interacting with a multi-user interactive touch display is challenging. We describe a technique for associating multi-touch interactions to individual users and their accelerometer-equipped mobile devices. Real-time device accelerometer data and depth camera-based body tracking are compared to associate each phone with a particular user, while body tracking and touch contacts positions are compared to associate a touch contact with a specific user. It is then possible to associate touch contacts with devices, allowing for more seamless device-display multi-user interactions. We detail the technique and present a user study to validate and demonstrate a content exchange application using this approach.

Instrumented Medical Shoes (MSs) are equipped with a variety of sensors for measurement of quantities such as pressure, acceleration, and temperature which are often greatly beneficial in numerous diagnosis, monitoring, rehabilitation, and other medical tasks. One of primary limiting factors of MSs is their energy sensitivity. In order to overcome this limitation, we have developed an optimization intensive approach for energy harvesting. Our goal is to size and position a single piezoelectric transducer for energy generation in a medical shoe in such a way that maximal energy is collected and/or specified maximal voltage is achieved while collecting energy. We propose a scenario approach that provides statistically sound solution and evaluate our approach using our medical shoe simulator for subject specific energy harvesting and generic MS scavenging. We could get 3.7X energy gain compare to smallest size sensor and 1.3X energy gain compared to sensor with the size of a shoe.

Sleep is an important part of our lives which affects many life factors such as memory, learning, metabolism and the immune system. Researchers have found correlations between sleep and several diseases such as Chronic Obstructive Pulmonary disease, Chronic Heart Failure, Alzheimer's disease, etc. However, sleep data is mainly recorded and diagnosed in sleep labs or in hospitals for some critical cases with high costs. In this work we develop a non-invasive, wearable neck-cuff system capable of real-time monitoring and visualization of physiological signals. These signals are generated from various sensors housed in a soft neck-worn collar and sent via Bluetooth to a cell phone which stores the data. This data is processed and reported to the user or uploaded to the cloud and/or to a local PC. With this system we are able to monitor people's sleep continuously in a non-invasive and low cost method while at the same time collect a large database for sleep data which may benefit future advances in new findings and possibly enable a diagnosis of other diseases. We show as one of the applications of our system the possible detection of obstructive sleep apnea which is a common sleep disorder.