Human activity recognition (HAR) has recently received significant attention due to its wide range of applications in health and activity monitoring. The nature of these applications requires mobile or wearable devices with limited battery capacity. User surveys show that charging requirement is one of the leading reasons for abandoning these devices. Hence, practical solutions must offer ultra-low power capabilities that enable operation on harvested energy. To address this need, we present the first fully integrated custom hardware accelerator (HAR engine) that consumes 22.4 μJ per operation using a commercial 65 nm technology. We present a complete solution that integrates all steps of HAR , i.e., reading the raw sensor data, generating features, and activity classification using a deep neural network (DNN). It achieves 95% accuracy in recognizing 8 common human activities while providing three orders of magnitude higher energy efficiency compared to existing solutions.

Objective : This study aims at investigating the effects of high stimulus presentation rates on the characteristics of c-VEP responses along with the change in performance depending on the stimulus presentation rate by utilizing a c-VEP based speller BCI. Approach : Twenty subjects participated in three different experiments with refresh rates of 60 Hz (E1), 120 Hz (E2) and 240 Hz (E3), where a 127-bit m-sequence is used. To observe the change in frequency content of c-VEP responses, PSD estimates of c-VEP responses were evaluated. Principal component analysis (PCA) was applied to observe how many distinguishable responses could be evoked with a 127-bit length m-sequence for three different refresh rates. Main Results : Average ITR and accuracy values are 85.87 bits min ⁻¹ and 92% for E1, 94.21 bits min ⁻¹ and 97% for E2, and 78.65 bits min ⁻¹ and 87% for E3 respectively. The c-VEP responses are band-limited although the bandwidth of the input signal significantly increases as the refresh rate increases. The spectral densities of c-VEP templates are concentrated on several frequency intervals, especially for E3, which eventually results in a target misclassification. PCA shows that the number of well distinguishable responses decreases with the increasing refresh rate. Considering all results and observations, we suggest that 120 Hz refresh rate is best to use in BCIs with high number of targets whereas 240 Hz refresh rate may be prefered for low number of targets. Significance: This study mainly investigates the alterations in the characteristics of c-VEP responses according to the stimulus presentation rate which have never been investigated thoroughly before. Our results show that increasing refresh rate does not necessarily increase the overall performance of the system due to the changes in characteristics of c-VEP responses. Further applications and designs of a c-VEP based BCIs will benefit from the observations and results of this study.

Energy harvesting offers an attractive and promising mechanism to power low-energy devices. However, it alone is insufficient to enable an energy-neutral operation, which can eliminate tedious battery charging and replacement requirements. Achieving an energy-neutral operation is challenging since the uncertainties in harvested energy undermine the quality of service requirements. To address this challenge, we present a runtime energy-allocation framework that optimizes the utility of the target device under energy constraints using a rollout algorithm, which is a sequential approach to solve dynamic optimization problems. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. The initial allocations are then corrected at every interval to compensate for the deviations from the expected energy harvesting pattern. We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users. Compared to prior techniques, the proposed framework achieves up to 35% higher utility even under energy-limited scenarios. Moreover, measurements on a wearable device prototype show that the proposed framework has $1000\times $ smaller energy overhead than iterative approaches with a negligible loss in utility.

Motion energy harvesting is an ideal alternative to battery in wearable applications since it can produce energy on demand. So far, widespread use of this technology has been hindered by bulky, inflexible and impractical designs. New flexible piezoelectric materials enable comfortable use of this technology. However, the energy harvesting potential of this approach has not been thoroughly investigated to date. This paper presents a novel mathematical model for estimating the energy that can be harvested from joint movements on the human body. The proposed model is validated using two different piezoelectric materials attached on a 3D model of the human knee. To the best of our knowledge, this is the first study that combines analytical modeling and experimental validation for joint movements. Thorough experimental evaluations show that 1) users can generate on average 13 μW power while walking, 2) we can predict the generated power with 4.8% modeling error.

Wearable devices with a flexible and stretchable form factor enable novel and attractive applications. The promising outlook for these devices is hindered by their limited battery capacity constrained by size and other physical limitations. Motion energy harvesting can alleviate these challenges and reduce dependency on batteries. Despite this potential, a definitive capacity for wearable motion energy harvesting has yet to be identified. To facilitate the exploration of this capacity, we propose an analytical model for the energy harvesting potential of a wearable piezoelectric energy harvester placed on the knee joint. In addition, we validate the proposed model extensively with a prototype piezoelectric energy harvester. Our results show that the proposed model estimates the harvested power with less than 5% mean absolute percentage error for all gait speeds and various transducer configurations. In addition, the proposed harvester can generate 161 $\mu \text{W}$ power at 5 mph. We further show that this amount can sustain a 35 $\mu \text{A}$ load.

Wearable devices have the potential to transform multiple facets of human life, including healthcare, activity monitoring, and interaction with computers. However, a number of technical and adaptation challenges hinder the widespread and daily usage of wearable devices. Recent research efforts have focused on identifying these challenges and solving them such that the potential of wearable devices can be realized. This monograph starts with a survey of the recent literature on the challenges faced by wearable devices. Then, it discusses potential solutions to each of the challenges. We start with the primary application areas that provide value to the users of wearable devices. We then present recent work on the design of physically flexible and bendable devices that aim to improve user comfort. We also discuss state-of-the-art energy harvesting and security solutions that can improve user compliance of wearable devices. Overall, this monograph aims to serve as a comprehensive resource for challenges and solutions towards self-powered wearable devices for health and activity monitoring.