Pervasive sensing enables diverse wearable event detection (WED) applications, but deploying machine learning models on resource-constrained microcontrollers (MCUs) poses significant challenges, particularly in ensuring prediction reliability under data shifts or out-of-distribution (OOD) inputs. While Uncertainty quantification methods offer a way to assess this reliability, many are computationally prohibitive for MCUs, and detecting multiple events concurrently further exacerbates resource constraints. Addressing these combined challenges, this paper presents an uncertainty and resource-aware framework designed for reliable and efficient multi-event WED on MCUs, significantly extending our preliminary work. The proposed framework achieves this by integrating Evidential Deep Learning (EDL) for efficient, single-pass uncertainty estimation with a novel cascade learning architecture. This architecture promotes resource efficiency via: (i) intra-event sharing using uncertainty-aware early exits within a staged model (shallow, medium, deep), allowing simpler samples to terminate inference earlier; and (ii) inter-event sharing using a multi-head design where multiple event detectors share a common backbone, minimizing overhead. System efficiency is further enhanced through MCU-specific optimizations, including targeted architecture search, quantization, efficient uncertainty operator implementation using standard TensorFlow Lite Micro (TFLM) operations, and library footprint reduction. We conducted extensive experiments on four distinct wearable datasets (Oesense, KWS, ECG5000, and HHAR) and two MCU platforms (STM32F446ZE, STM32H747XI), comparing the proposed framework against strong baselines including Deep Ensembles and Vanilla EDL. Results demonstrate the proposed framework’s effectiveness, achieving competitive accuracy and uncertainty performance (e.g., up to 22% lower NLL than data augmentation) while drastically reducing resource consumption, offering up to 8.64 × faster inference, up to 8.57 × lower energy use, and 55% smaller memory footprint compared to ensemble methods. The proposed framework enables the deployment of reliable, uncertainty-aware multi-event detection on a wider range of low-power MCUs.

Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developed EarSD , a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring. EarSD is an integrated custom-built sensing - computing - communication ear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby. EarSD ’s ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality.

Stress has emerged and continues to be a regular obstacle in people's lives. When left ignored and untreated, it can lead to many health complications, including an increased risk of death. In this study, we propose a foundation model approach for stress detection without the need to train the model from scratch. Specifically, we utilise the foundation model "Neuro-GPT", which was trained on a large open dataset (TUH EEG) with 20,000 EEG recordings. We fine-tune the model for stress detection and evaluate it on a 40-subject open stress dataset. The evaluation results with a fine-tuned Neuro-GPT are promising with an average accuracy of 74.4% in quantifying "low-stress" and "high-stress". We also conducted experiments to compare the foundation model approach with traditional machine learning methods and highlight several observations for future research in this direction.

Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers (MCUs). This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection. In this paper, we present UR2M, a novel Uncertainty and Resource-aware event detection framework for MCUs. Specifically, we (i) develop an uncertainty-aware WED based on evidential theory for accurate event detection and reliable uncertainty estimation; (ii) introduce a cascade ML framework to achieve efficient model inference via early exits, by sharing shallower model layers among different event models; (iii) optimize the deployment of the model and MCU library for system efficiency. We conducted extensive experiments and compared UR2M to traditional uncertainty baselines using three wearable datasets. Our results demonstrate that UR2M achieves up to 864% faster inference speed, 857% energy-saving for uncertainty estimation, 55% memory saving on two popular MCUs, and a 22% improvement in uncertainty quantification performance. UR2M can be deployed on a wide range of MCUs, significantly expanding real-time and reliable WED applications.

Every year, the U.S. economy loses more than${\$}$411 billion because of work performance reduction, injuries, and traffic accidents caused by microsleep. To mitigate microsleep's consequences, an unobtrusive, reliable, and socially acceptable microsleep detection solution throughout the day, every day is required. Unfortunately, existing solutions do not meet these requirements. In this paper, we propose WAKE, a novel behind-the-ear wearable device for microsleep detection. By monitoring biosignals from the brain, eye movements, facial muscle contractions, and sweat gland activities from behind the user's ears, WAKE can detect microsleep with a high temporal resolution. We introduce a Three-fold Cascaded Amplifying (3CA) technique to tame the motion artifacts and environmental noises for capturing high fidelity signals. Through our prototyping, we show that WAKE can suppress motion and environmental noise in real-time by 9.74-19.47 dB while walking, driving, or staying in different environments, ensuring that the biosignals are captured reliably. We evaluated WAKE using gold-standard devices on 19 sleep-deprived and narcoleptic subjects. The Leave-One-Subject-Out Cross-Validation results show the feasibility of WAKE in microsleep detection on an unseen subject with average precision and recall of 76 and 85 percent, respectively.

We present a low-maintenance, wind-powered, battery-free, biocompatible, tree wearable, and intelligent sensing system, namely IoTree, to monitor water and nutrient levels inside a living tree. IoTree system includes tiny-size, biocompatible, and implantable sensors that continuously measure the impedance variations inside the living tree's xylem, where water and nutrients are transported from the root to the upper parts. The collected data are then compressed and transmitted to a base station located at up to 1.8 kilometers (approximately 1.1 miles) away. The entire IoTree system is powered by wind energy and controlled by an adaptive computing technique called block-based intermittent computing, ensuring the forward progress and data consistency under intermittent power and allowing the firmware to execute with the most optimal memory and energy usage. We prototype IoTree that opportunistically performs sensing, data compression, and long-range communication tasks without batteries. During in-lab experiments, IoTree also obtains the accuracy of 91.08% and 90.51% in measuring 10 levels of nutrients, NH3 and K2O, respectively. While tested with Burkwood Viburnum and White Bird trees in the indoor environment, IoTree data strongly correlated with multiple watering and fertilizing events. We also deployed IoTree on a grapevine farm for 30 days, and the system is able to provide sufficient measurements every day.

In this paper, we present a low-maintenance, wind-powered, battery-free, biocompatible, tree wearable, and intelligent sensing system, namely IoTree, to monitor water and nutrient levels inside a living tree. IoTree system includes tiny-size, biocompatible, and implantable sensors that continuously measure the impedance variations inside the living tree's xylem, where water and nutrients are transported from the root to the upper parts. The collected data are then compressed and transmitted to a base station located at up to 1.8 kilometers (approximately 1.1 miles) away. The entire IoTree system is powered by wind energy and controlled by an adaptive computing technique called block-based intermittent computing, ensuring the forward progress and data consistency under intermittent power and allowing the firmware to execute with the most optimal memory and energy usage. We prototype IoTree that opportunistically performs sensing, data compression, and long-range communication tasks without batteries. During in-lab experiments, IoTree also obtains the accuracy of 91.08% and 90.51% in measuring 10 levels of nutrients, NH3 and K2O, respectively. While tested with Burkwood Viburnum and White Bird trees in the indoor environment, IoTree data strongly correlated with multiple watering and fertilizing events. We also deployed IoTree on a grapevine farm for 30 days, and the system is able to provide sufficient measurements every day.

While the global healthcare market of wearable devices has been growing significantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime, slow response rate, and inadequate biosignal quality.

Microsleep, caused by sleep deprivation, sleep apnea, and narcolepsy, costs the U.S.'s economy more than $411 billion/year because of work performance reduction, injuries, and traffic accidents. Mitigating microsleep's consequences require an unobtrusive, reliable, and socially acceptable microsleep detection solution throughout the day, every day. Unfortunately, existing solutions do not meet these requirements.

Over 50 million people undergo surgeries each year in the United States, with over 70% of them filling opioid prescriptions within one week of the surgery. Due to the highly addictive nature of these opiates, a post-surgical window is a crucial time for pain management to ensure accurate prescription of opioids. Drug prescription nowadays relies primarily on self-reported pain levels to determine the frequency and dosage of pain drug. Patient pain self-reports are, however, influenced by subjective pain tolerance, memories of past painful episodes, current context, and the patient's integrity in reporting their pain level. Therefore, objective measures of pain are needed to better inform pain management.

Drones have carried weapons, drugs, explosives and illegal packages in the recent past, raising strong concerns from public authorities. While existing drone monitoring systems only focus on detecting drone presence, localizing or fingerprinting the drone, there is a lack of a solution for estimating the additional load carried by a drone. In this paper, we present a novel passive RF system, namely DroneScale, to monitor the wireless signals transmitted by commercial drones and then confirm their models and loads. Our key technical contribution is a proposed technique to passively capture vibration at high resolution (i.e., 1Hz vibration) from afar, which was not possible before. We prototype DroneScale using COTS RF components and illustrate that it can monitor the body vibration of a drone at the targeted resolution. In addition, we develop learning algorithms to extract the physical vibration of the drone from the transmitted signal to infer the model of a drone and the load carried by it. We evaluate the DroneScale system using 5 different drone models, which carry external loads of up to 400g. The experimental results show that the system is able to estimate the external load of a drone with an average accuracy of 96.27%. We also analyze the sensitivity of the system with different load placements with respect to the drone's body, flight modes, and distances up to 200 meters.

Frequent blood pressure (BP) assessment is key to the diagnosis and treatment of many severe diseases, such as heart failure, kidney failure, hypertension, and hemodialysis. Current "gold-standard'' BP measurement techniques require the complete blockage of blood flow, which causes discomfort and disruption to normal activity when the assessment is done repetitively and frequently. Unfortunately, patients with hypertension or hemodialysis often have to get their BP measured every 15 minutes for a duration of 4-5 hours or more. The discomfort of wearing a cumbersome and limited mobility device affects their normal activities. In this work, we propose a device called eBP to measure BP from inside the user's ear aiming to minimize the measurement's impact on users' normal activities while maximizing its comfort level. eBP has 3 key components: (1) a light-based pulse sensor attached on an inflatable pipe that goes inside the ear, (2) a digital air pump with a fine controller, and (3) a BP estimation algorithm. In contrast to existing devices, eBP introduces a novel technique that eliminates the need to block the blood flow inside the ear, which alleviates the user's discomfort. We prototyped eBP custom hardware and software and evaluated the system through a comparative study on 35 subjects. The study shows that eBP obtains the average error of 1.8 mmHg and -3.1 mmHg and a standard deviation error of 7.2 mmHg and 7.9 mmHg for systolic (high-pressure value) and diastolic (low-pressure value), respectively. These errors are around the acceptable margins regulated by the FDA's AAMI protocol, which allows mean errors of up to 5 mmHg and a standard deviation of up to 8 mmHg.

Cognitive state monitoring is crucial for neurological disorders such as epilepsy, narcolepsy, insomnia, and many other human health concerns. The capability to continuously monitor an individual wearing the device and accurately provide early warnings of seizures or narcolepsy sleep attacks would be game-changing for these disorders. Beyond human health, complete hand-free/voice-free human-computer interaction is desirable for privacy-sensitive use cases or people with disabilities. To achieve this goal, we propose Earable, an ear-worn biosensing platform for cognitive state quantification and human-computer interaction. Earable can capture biosignal including brain waves activities, eyes movements, and facial muscle contractions from the back of the ears. Its form factor is convenient to use in everyday life. In this demo, we show two use cases for our Earable platform. First, as an example of cognitive state monitoring, our system plays relaxing music and dims the light when the user is trying to relax or sleep by detecting alpha and beta waves generated by the brain. Second, as an example of human-computer interaction, our system controls a drone with eye movements and facial muscle activity.

Since the concept of Cognitive Radio (CR) was first introduced, it has been considered as a key technology of future wireless devices to better utilize radio spectrum. A number of CR-MAC protocols have been studied for Cognitive Radio Networks (CRNs), but most state-of-the-art frequencyhopping based protocols focus mainly on channel mobility and spectrum resource allocation. They lack integration of other essential features such as time synchronization and cooperative spectrum sensing, which are practically crucial, into a full-blown CR-MAC protocol. In this paper, we propose MSHCS-MAC: a mac protocol for Multi-hop CRNs based on Slow Hopping and Cooperative Sensing approach which is the cutting edge frequency hopping scheme. The contributions of MSHCS-MAC are threefold: (1) Support of multi-hop communication without dedicated control channel and multiple transceivers, (2) Integration of essential CR-MAC features such as bootstrapping, multi-channel operation, cooperative spectrum sensing and time synchronization, (3) Practical implementation and evaluation on commercial devices. The evaluation results show that MSHCS-MAC provides reasonable performance in the experimental testbed with supporting multihop communication and essential CR-MAC features.