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The potential of energy harvesting and batteryless sensing promises a future where IoT devices will become sustainable, long-lasting, and maintenance free. Given the significant challenges involved with building and programming such devices, it is reasonable to question whether energy harvesting based IoT can replace existing wearables (fitness trackers, smartwatches, or medical devices), while providing a reliable user experience. Hence, an important question arises: “What role can energy harvesting based sensing play in the age of AI and deep learning?” While energy harvesting based sensors can unlock new applications in wearables and personalized data analytics, the path towards integrating them into the modern deep learning landscape requires substantial intellectual innovation. This workshop aims to bridge multiple perspectives in wearable sensing and data analytics in the modern age of AI. 

Batteryless sensing devices which rely on energy harvesting can enable more sustainable and long-lasting Internet of Things (IoT) based wearables. While it has become feasible to implement energy-harvesting based wearables for digital health applications, it remains challenging to integrate such devices and the data they collect into machine learning pipelines for tasks such as human activity recognition (HAR). A key obstacle is uncertainty in the data acquisition process. Given the discontinuous and uncertain availability of harvested energy, when should a sensor spend energy to sample and transmit data packets for processing? A common approach is to spend energy opportunistically by sending packets whenever sufficient energy is available. However, when considering a specific task, namely HAR with kinetic energy harvesting based sensors, this approach unfairly prioritizes data from activities where more energy can be harvested (e.g., running). In this work, we improve the opportunistic energy spending policy by pruning redundant packets to reallocate energy towards activities where less energy is harvested. Our approach results in an increase in the F1-score of ‘lower energy’ activities while having a minimal impact on the F1-score of ‘higher energy’ activities. 

In deep learning (DL) based human activity recognition (HAR), sensor selection seeks to balance prediction accuracy and sensor utilization (how often a sensor is used). With advances in on-device inference, sensors have become tightly integrated with DL, often restricting access to the underlying model used. Given only sensor predictions, how can we derive a selection policy which does efficient classification while maximizing accuracy? We propose a cascaded inference approach which, given the prediction of any one sensor, determines whether to query all other sensors. Typically, cascades use a sequence of classifiers which terminate once the confidence of a classifier exceeds a threshold. However, a threshold-based policy for sensor selection may be suboptimal; we define a more general class of policies which can surpass the threshold. We extend to settings where little or no labeled data is available for tuning the policy. Our analysis is validated on three HAR datasets by improving upon the F1-score of a threshold policy across several utilization budgets. Overall, our work enables practical analytics for HAR by relaxing the requirement of labeled data for sensor selection and reducing sensor utilization to directly extend a sensor system’s lifetime. 

Wearable IoT devices rely on batteries, which pose challenges for long-term sustainable health monitoring due to the need for recharging or replacement. Batteryless sensing approaches, which harvest energy from the environment, offer an appealing alternative. However, given the discontinuous supply of harvested energy, it is unclear how to leverage sparse, asynchronous data from batteryless sensors for machine learning (ML) tasks such as human activity recognition (HAR). To this end, we present and profile a prototype of a system to simulate data acquisition from a set of kinetic energy harvesting devices. Our results demonstrate that there is a need to jointly optimize (1) when sensors should spend energy to communicate data, and (2) the training of the ML model that will receive the data. 

A central challenge in machine learning deployment is maintaining accurate and updated models as the deployment environment changes over time. We present a hardware/software framework for simultaneous training and inference for monocular depth estimation on edge devices. Our proposed framework can be used as a hardware/software co-design tool that enables continual and online federated learning on edge devices. Our results show real-time training and inference performance, demonstrating the feasibility of online learning on edge devices.