Recent advancements in low-power electronics and machine-learning techniques have paved the way for innovative wearable Internet of Things (IoT) devices. However, these devices suffer from limited battery capacity and computational power. Hence, energy harvesting from ambient sources has emerged as a promising solution for powering low-energy wearables. Optimal management of the harvested energy is crucial for achieving energy-neutral operation and eliminating the need for frequent recharging. This task is challenging due to the dynamic nature of harvested energy and battery energy constraints. To tackle this challenge, we propose tinyMAN, a reinforcement learning-based energy management framework for resource-constrained wearable IoT devices. tinyMAN maximizes the target device utilization under battery energy constraints without relying on the harvested energy forecast, making it a prediction-free approach. It achieves up to 17% higher utility while reducing battery constraint violations by 80% compared to prior work. We also introduce tinyMAN-MO, a multi-objective extension of tinyMan for applications with time-varying energy demands. It learns the tradeoff between meeting the application’s energy demand and maintaining the battery energy level. We deployed our framework on a wearable device prototype using TensorFlow Lite for Micro, leveraging its small (less than 120 KB) memory footprint. Evaluations show that tinyMAN-MO operates within 10% of the Pareto-optimal solutions with only 1.98 ms execution time and 23.17 μJ energy consumption overhead.

Energy harvesting (EH) and management (EM) have emerged as enablers of self-sustained wearable devices. Since EH alone is not sufficient for self-sustainability due to uncertainties of ambient sources and user activities, there is a critical need for a user-independent EM approach that does not rely on expected EH predictions. We present a generalized energy management framework (GEM-RL) using multi-objective reinforcement learning. GEM-RL learns the trade-off between utilization and the battery energy level of the target device under dynamic EH patterns and battery conditions. It also uses a lightweight approximate dynamic programming (ADP) technique that utilizes the trained MORL agent to optimize the utilization of the device over a longer period. Thorough experiments show that, on average, GEM-RL achieves Pareto front solutions within 5.4% of the offline Oracle for a given day. For a 7-day horizon, it achieves utility up to 4% within the offline Oracle and up to 50% higher utility compared to baseline EM approaches. The hardware implementation on a wearable device shows negligible execution time (1.98 ms) and energy consumption (23.17 μJ) overhead.

Cattle diseases have a significant negative impact not only on the animals' welfare but also on the economic performance of the cattle industry [1], [2]. For example, Bovine Respiratory Disease is responsible for approximately 75% of the morbidity and 57% of the mortality in US feedlots, which is estimated to cost the agriculture industry about $1B annually [1], [2]. The current management practice to diagnose and select cattle for treatment is a widespread clinical scoring system called DART (Depression, Appetite, Respiration, and Temperature). DART requires manual labor and skilled personnel, which is a limiting factor due to labor-shortage in several industry sectors, including agriculture [3]. Therefore, a continuous and automated IoT solution to predict the health state of a cow is a critical tool for the cattle industry.

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. The proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

Artificial intelligence (AI) based wearable applications collect and process a significant amount of streaming sensor data. Transmitting the raw data to cloud processors wastes scarce energy and threatens user privacy. Wearable edge AI devices should ideally balance two competing requirements: (1) maximizing the energy efficiency using targeted hardware accelerators and (2) providing versatility using general-purpose cores to support arbitrary applications. To this end, we present an open-source domain-specific programmable system-on-chip (SoC) that combines a RISC-V core with a meticulously determined set of accelerators targeting wearable applications. We apply the proposed design method to design an FPGA prototype and six real-life use cases to demonstrate the efficacy of the proposed SoC. Thorough experimental evaluations show that the proposed SoC provides up to 9.1 × faster execution and up to 8.9 × higher energy efficiency than software implementations in FPGA while maintaining programmability.

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 andAmerican Time Use Surveydata 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.

Movement disorders, such as Parkinson’s disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step and stride lengths provide valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, this article presents a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. Experimental results show that the proposed model estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.

Wearable devices can enable affordable and accessible smart health care services with the help of innovative low-power design and edge computing technologies. Indeed, novel wearable devices are already fueling a shift from a hospital-centric setting to more personalized home-based solutions [1]. A wide variety of miniature, flexible, and stretchable sensors enable collecting real-time data without impeding users’ daily routines. For example, inertial measurement units (IMUs) based on MEMS technology integrate a 9-axis accelerometer/gyroscope/magnetometer into a small package. Similarly, bend and stretch sensors embedded into clothes measure knee and hip angles, while biosensors track biopotentials, such as electrocardiogram (ECG) and electromyography (EMG). Then, novel edge-AI algorithms process the real-time data using low-power processors to build smart health applications ranging from health and activity monitoring to early diagnosis and prognosis [2] (Section B). One of the most critical challenges in wearable smart health applications is the stringent energy capacity imposed by size and weight constraints [2], [3]. All the required sensing, processing, and communications tasks must be performed without any manual charging or battery maintenance effort to maximize the user experience (Section C). The rest of this extended abstract discusses the challenges and potential solutions for driver applications and energy management techniques.

Emerging flexible and stretchable devices open up novel and attractive applications beyond traditional rigid wearable devices. Since the small and flexible form-factor severely limits the battery capacity, energy harvesting (EH) stands out as a critical enabler of new devices. Despite increasing interest in recent years, the capacity of wearable energy harvesting remains unknown. Prior work analyzes the power generated by a single and typically rigid transducer. This choice limits the EH potential and undermines physical flexibility. Moreover, current results do not translate to total harvested energy over a given period, which is crucial from a developer perspective. In contrast, this paper explores the daily energy harvesting potential of combining flexible light and motion energy harvesters. It first presents a multi-modal energy harvesting system design whose inputs are flexible photo-voltaic cells and piezoelectric patches. We measure the generated power under various light intensity and gait speeds. Finally, we construct daily energy harvesting patterns of 9593 users by integrating our measurements with the activity data from the American Time Use Survey. Our results show that the proposed system can harvest on average 0. 6mAh @ 3. 6V per day.

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.

Recent developments in stretchable and flexible sensing and processing technologies enable a wide range of wearable devices. These devices can pave the way to medical applications ranging from health and activity monitoring to diagnosis and treatments of movement disorders. However, recent studies show that this potential is hindered by both adaptation challenges that affect the end users and technology challenges faced by developers. This paper first summarizes the challenges faced by wearable devices targeting health and user activity monitoring applications. Then, it reviews recent research progress towards addressing these challenges in energy harvesting, energy management, flexible system design, and test areas.

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.