Longitudinal and continuous monitoring of cough is crucial for early and accurate diagnosis of respiratory diseases. While recent developments in wearables offer a promise for daily assessment at-home remote symptom monitoring with respect to more accurate and less frequent assessment in the clinics, important practical challenges exist such as maintaining user speech privacy and potential poor audio quality and background noise in uncontrolled real-world settings. This study addresses these challenges by developing and optimizing a compact multimodal cough detection system, enhanced with an Out-of-Distribution (OOD) detection algorithm. The cough sensing modalities include audio and Inertial Measurement Unit (IMU) signals. We optimized this multimodal cough detection system by training with an enhanced dataset and employing a weighted multi-loss approach for the ID classifier. For OOD detection, we improved the system by reconstructing the training data components. Our preliminary results indicate the robustness of the system across window sizes from 1 to 5 seconds and performs efficiently at low audio frequencies, which can protect user privacy due to illegibility or incomprehensibility at lower sampling rates. Although we found that the multimodal model is sensitive to OOD data, the final optimized robust multimodal cough detection system outperforms the singlemodal model integrated with OOD detection. Specifically, the optimized system maintains 90.08% accuracy and a cough F1 score of 0.7548 at a 16 kHz audio frequency, and 87.3% accuracy and a cough F1 score of 0.7015 at 750 Hz, even with half of the data being OOD during inference. The misclassified components mainly originate from nonverbal sounds, including sneezes and groans. These issues could be further mitigated by acquiring more data on cough, speech, and other nonverbal vocalizations. In general, we observed that the Audio-IMU multimodal model incorporating OOD detection techniques significantly improved cough detection performance and could provide a tangible solution to real-world acoustic cough detection problems.

Cough is an important defense mechanism of the respiratory system and is also a symptom of lung diseases, such as asthma. Acoustic cough detection collected by portable recording devices is a convenient way totrack potential condition worsening for patients who have asthma. However, the data used in building current cough detection models are often clean, containing a limited set of sound categories, and thus perform poorly when they are exposed to a variety of real-world sounds which could be picked up by portable recording devices. The sounds that are not learned by the model are referred to as Out-of-Distribution (OOD) data. In this work, we propose two robust cough detection methods combined with an OOD detection module, that removes OOD data without sacrificing the cough detection performance of the original system. These methods include adding a learning confidence parameter and maximizing entropy loss. Our experiments show that 1) the OOD system can produce dependable In-Distribution (ID) and OOD results at a sampling rate above 750 Hz; 2) the OOD sample detection tends to perform better for larger audio window sizes; 3) the model's overall accuracy and precision get better as the proportion of OOD samples increase in the acoustic signals; 4) a higher percentage of OOD data is needed to realize performance gains at lower sampling rates. The incorporation of OOD detection techniques improves cough detection performance by a significant margin and provides a valuable solution to real-world acoustic cough detection problems.

In this paper, we present our efforts towards packaging of a novel compressive sampling (CS) based ultra-low power photoplethysmography (PPG) application-specific integrated circuit (ASIC) into a wearable form factor. The system comprises of a custom PPG analog front-end circuit, integrated with a digital back-end to enable CS, and a commercial off-the-shelf (COTS) system-on-chip (SoC) for Bluetooth Low Energy (BLE) based wireless data transfer. The ASIC consumes$172 [\pmb \mu \mathrm{W}$power to extract heart rate from the sparse PPG signal where the whole system consumes 1.66 mW power for continuous streaming of heart rate data over the COTS BLE radio. This work presents the first ever demonstration of a CS based PPG ASIC in wrist-band form factors and paves our way towards deploying and evaluating this custom PPG ASIC in future clinical studies. The modular architecture of the wristband platform allows for incorporation of other sensors for future correlated sensing studies between health and environment.

This paper demonstrates the ability to accurately detect the movement state of Madagascar hissing cockroaches equipped with a custom board containing a five degree of freedom inertial measurement unit. The cockroach moves freely through an unobstructed arena while wirelessly transmitting its accelerometer and gyroscope data. Multiple window sizes, features, and classifiers are assessed. An in-depth analysis of the classification results is performed to better understand the strengths and weaknesses of the classifier and feature set. The conclusions of this study show promise for future work on cockroach motion mode identification and localization.

This paper presents preliminary results for motion behavior analysis of Madagascar hissing cockroach biobots subject to stochastic and periodic neurostimulation pulses corresponding to randomly applied right and left turn, and move forward commands. We present our experimental setup and propose an unguided search strategy based stimulation profile designed for exploration of unknown environments. We study a probabilistic motion model fitted to the trajectories of biobots, perturbed from their natural motion by the stimulation pulses. Furthermore, we provide a statistical assessment of the biobotic directional response to turn commands and its correlation with stimuli profile over time. This study paves the way towards reliable control for more realistic under-rubble search and rescue applications.

Although numerous advances have been made in instrumental odor detection systems, these still cannot match the efficient sampling, odor discrimination, agile mobility and the olfactory acuity of odor detection dogs. A limiting step in using dogs to detect odors is the subjectivity of the translation of odor information processed by the dog to its handler. We present our preliminary efforts towards a wireless wearable system for continuous auscultation of respiratory behavior by recording internal sounds at the neck and chest by means of a commercially available electronic stethoscope to provide objective decision support for handlers. We have identified discrete features of sniffing and panting in the time domain and utilize event duration, event rate, event mean energy, and the number of consecutive events in a row to build a decision tree classifier. Since feature extraction requires segmentation of individual sniffing and panting events, we developed an adaptive method using short-time energy contour and an adaptive threshold. The performance of the system was evaluated on recordings from a Greyhound and a Labrador Retriever and achieved high classification accuracies.

We present our efforts toward enabling a wearable sensor system that allows for the correlation of individual environmental exposures with physiologic and subsequent adverse health responses. This system will permit a better understanding of the impact of increased ozone levels and other pollutants on chronic asthma conditions. We discuss the inefficiency of existing commercial off-the-shelf components to achieve continuous monitoring and our system-level and nano-enabled efforts toward improving the wearability and power consumption. Our system consists of a wristband, a chest patch, and a handheld spirometer. We describe our preliminary efforts to achieve a submilliwatt system ultimately powered by the energy harvested from thermal radiation and motion of the body with the primary contributions being an ultralow-power ozone sensor, an volatile organic compounds sensor, spirometer, and the integration of these and other sensors in a multimodal sensing platform. The measured environmental parameters include ambient ozone concentration, temperature, and relative humidity. Our array of sensors also assesses heart rate via photoplethysmography and electrocardiography, respiratory rate via photoplethysmography, skin impedance, three-axis acceleration, wheezing via a microphone, and expiratory airflow. The sensors on the wristband, chest patch, and spirometer consume 0.83, 0.96, and 0.01 mW, respectively. The data from each sensor are continually streamed to a peripheral data aggregation device and are subsequently transferred to a dedicated server for cloud storage. Future work includes reducing the power consumption of the system-on-chip including radio to reduce the entirety of each described system in the submilliwatt range.

One of the challenges to working with canines is that whereas humans are primarily vocal communicators, canines are primarily postural and behavioral communicators. It can take years to gain some level of proficiency at reading canine body language, even under the best of circumstances. In the case of guide dogs and visually-impaired handlers, this task is even more difficult. Luckily, new technology designed to help monitor canines may prove useful in helping handlers, especially those with visual impairments, to better understand and interpret what their working partners are feeling or saying.

Training animals is a process that requires a significant investment of time and energy on the part of the trainer. One of the most basic training tasks is to train dogs to perform postures on cue. While it might be easy for a human trainer to see when an animal has performed the desired posture, it is much more difficult for a computer to determine this. Most work in this area uses accelerometer and/or gyroscopic data to produce data from an animal's current state, but this has limitations. Take for example a normal standing posture. From an accelerometer's perspective, it closely resembles the "laying down" posture, but the posture can look very different if the animal is standing still, versus walking, versus running, and might look completely different from a "standing on incline" posture. A human trainer can instantly tell the difference between these postures and behaviors, but the process is much more difficult for a computer.

We present a wearable sensor system consisting of a wristband and chest patch to enable the correlation of individual environmental exposure to health response for understanding impacts of ozone on chronic asthma conditions. The wrist worn device measures ambient ozone concentration, heart rate via plethysmography (PPG), three-axis acceleration, ambient temperature, and ambient relative humidity. The chest patch measures heart rate via electrocardiography (ECG) and PPG, respiratory rate via PPG, wheezing via a microphone, and three-axis acceleration. The data from each sensor is continually streamed to a peripheral data aggregation device, and is subsequently transferred to a dedicated server for cloud storage. The current generation of the system uses only commercially-off-the-shelf (COTS) components where the entire electronic structure of the wristband has dimensions of 3.1×4.1×1.2 cm3 while the chest patch electronics has a dimensions of 3.3×4.4×1.2 cm3. The power consumptions of the wristband and chest patch are 78 mW and 33 mW respectively where using a 400 mAh lithium polymer battery would operate the wristband for around 15 hours and the chest patch for around 36 hours.

Health care continues to be one of the biggest challenges facing our society. Factors such as lifestyle choices, genetics, aging, stress and environmental exposures play a critical role in determining health outcomes. Wearable technologies that can enable continuous/long-term personal health monitoring and personal environmental monitoring can empower users to make better lifestyle decisions and improve health outcomes. While wearable devices promise a compelling future of achieving wellness, current wearable products are not addressing the needs of the health space. To achieve this future, key challenges in wearable systems such as battery life, form factor, sensor functionality, configurability and data analysis will have to be carefully addressed to ensure user adoption and effectively manage health.

In this demonstration, we present a topological mapping system to be used with biobotic insects in order to sketch maps of unknown arenas using only neighbor to neighbor interactions among the agents. Biobotic insects fuse the locomotory advantages of insects with wireless sensing technology in form of electronic backpacks to function as search and rescue agents. Our mapping approach is designed for emergency response scenarios, where traditional mapping approaches may fail due to lack of localization information. We demonstrate the performance of our proposed approach instead using Hexbugs, which emulate the natural random motion of biobots. The Hexbugs are dispersed into a maze with unknown structure (Fig. 1), and their local interactions are captured through a visual tracking system. Such information is then exploited into a data analysis engine in order to robustly find the topological structure of the maze.

This article provides the latest advances from the NSF Advanced Self-powered Systems of Integrated sensors and Technologies (ASSIST) center. The work in the center addresses the key challenges in wearable health and environmental systems by exploring technologies that enable ultra-long battery lifetime, user comfort and wearability, robust medically validated sensor data with value added from multimodal sensing, and access to open architecture data streams. The vison of the ASSIST center is to use nanotechnology to build miniature, self-powered, wearable, and wireless sensing devices that can enable monitoring of personal health and personal environmental exposure and enable correlation of multimodal sensors. These devices can empower patients and doctors to transition from managing illness to managing wellness and create a paradigm shift in improving healthcare outcomes. This article presents the latest advances in high-efficiency nanostructured energy harvesters and storage capacitors, new sensing modalities that consume less power, low power computation, and communication strategies, and novel flexible materials that provide form, function, and comfort. These technologies span a spatial scale ranging from underlying materials at the nanoscale to body worn structures, and the challenge is to integrate them into a unified device designed to revolutionize wearable health applications.

Training and handling working dogs is a costly process and requires specialized skills and techniques. Less subjective and lower-cost training techniques would not only improve our partnership with these dogs but also enable us to benefit from their skills more efficiently. To facilitate this, we are developing a canine body-area-network (cBAN) to combine sensing technologies and computational modeling to provide handlers with a more accurate interpretation for dog training. As the first step of this, we used inertial measurement units (IMU) to remotely detect the behavioral activity of canines. Decision tree classifiers and Hidden Markov Models were used to detect static postures (sitting, standing, lying down, standing on two legs and eating off the ground) and dynamic activities (walking, climbing stairs and walking down a ramp) based on the heuristic features of the accelerometer and gyroscope data provided by the wireless sensing system deployed on a canine vest. Data was collected from 6 Labrador Retrievers and a Kai Ken. The analysis of IMU location and orientation helped to achieve high classification accuracies for static and dynamic activity recognition.