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Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization. 

The use of wearable and mobile devices for health and activity monitoring is growing rapidly. These devices need to maximize their accuracy and active time under a tight energy budget imposed by battery and form-factor constraints. This paper considers energy harvesting devices that run on a limited energy budget to recognize user activities over a given period. We propose a technique to co-optimize the accuracy and active time by utilizing multiple design points with different energy-accuracy trade-offs. The proposed technique switches between these design points at runtime to maximize a generalized objective function under tight harvested energy budget constraints. We evaluate our approach experimentally using a custom hardware prototype and 14 user studies. It achieves 46% higher expected accuracy and 66% longer active time compared to the highest performance design point. 

Advances in integrated sensors and low-power electronics have led to an increase in the use of wearable devices for health and activity monitoring applications. These devices have severe limitations on weight, form-factor, and battery size since they have to be comfortable to wear. Therefore, they must minimize the total platform energy consumption while satisfying functionality (e.g., accuracy) and performance requirements. Optimizing the platform-level energy efficiency requires considering both the sensor and processing subsystems. To this end, this paper presents a sensor-classifier co-optimization technique with human activity recognition as a driver application. The proposed technique dynamically powers down the accelerometer sensors and controls their sampling rate as a function of the user activity. It leads to a 49% reduction in total platform energy consumption with less than 1% decrease in activity recognition accuracy. 

Flexible hybrid electronics (FHE) is emerging as a promising solution to combine the benefits of printed electronics and silicon technology. FHE has many high-impact potential areas, such as wearable applications, health monitoring, and soft robotics, due to its physical advantages, which include light weight, low cost and the ability conform to different shapes. However, physical deformations in the field can lead to significant testing and validation challenges. For example, designers must ensure that FHE devices continue to meet their specs even when the components experience stress due to bending. Hence, physical deformation, which is hard to emulate, has to be part of the test procedures for FHE devices. This paper is the first to analyze stress experience at different parts of FHE devices under different bending conditions. We develop a novel methodology to maximize the test coverage with minimum number of text vectors with the help of a mixed integer linear programming formulation. We validate the proposed approach using an FHE prototype and COMSOL Multiphysics simulations. 

Human activity recognition (HAR) has attracted significant research interest due to its applications in health monitoring and patient rehabilitation. Recent research on HAR focuses on using smartphones due to their widespread use. However, this leads to inconvenient use, limited choice of sensors and inefficient use of resources, since smartphones are not designed for HAR. This paper presents the first HAR framework that can perform both online training and inference. The proposed framework starts with a novel technique that generates features using the fast Fourier and discrete wavelet transforms of a textile-based stretch sensor and accelerometer data. Using these features, we design a neural network classifier which is trained online using the policy gradient algorithm. Experiments on a low power IoT device (TI-CC2650 MCU) with nine users show 97.7% accuracy in identifying six activities and their transitions with less than 12.5 mW power consumption. 

Small form factor and low-cost wearable devices enable a variety of applications including gesture recognition, health monitoring, and activity tracking. Energy harvesting and optimal energy management are critical for the adoption of these devices, since they are severely constrained by battery capacity. This paper considers optimal gesture recognition using self-powered devices. We propose an approach to maximize the number of gestures that can be recognized under energy budget and accuracy constraints. We construct a computationally efficient optimization algorithm with the help of analytical models derived using the energy consumption breakdown of a wearable device. Our empirical evaluations demonstrate up to 2.4 x increase in the number of recognized gestures compared to a manually optimized solution. 

Wearable internet of things (IoT) devices are becoming popular due to their small form factor and low cost. Potential applications include human health and activity monitoring by embedding sensors such as accelerometer, gyroscope, and heart rate sensor. However, these devices have severely limited battery capacity, which requires frequent recharging. Harvesting ambient energy and optimal energy allocation can make wearable IoT devices practical by eliminating the charging requirement. This paper presents a near-optimal runtime energy management technique by considering the harvested energy. The proposed solution maximizes the performance of the wearable device under minimum energy constraints. We show that the results of the proposed algorithm are, on average, within 3% of the optimal solution computed offline. 

Wearable devices with sensing, processing and communication capabilities have become feasible with the advances in internet-of-things (IoT) and low power design technologies. Energy harvesting is extremely important for wearable IoT devices due to size and weight limitations of batteries. One of the most widely used energy harvesting sources is photovoltaic cell (PV-cell) owing to its simplicity and high output power. In particular, flexible PV-cells offer great potential for wearable applications. This paper models, for the first time, how bending a PV-cell significantly impacts the harvested energy. Furthermore, we derive an analytical model to quantify the harvested energy as a function of the radius of curvature. We validate the proposed model empirically using a commercial PV-cell under a wide range of bending scenarios, light intensities and elevation angles. Finally, we show that the proposed model can accelerate maximum power point tracking algorithms and increase the harvested energy by up to 25.0%.