Precision Swine Farming has the potential to directly benefit swine health and industry profit by automatically monitoring the growth and health of pigs. We introduce the first system to use structural vibration to track animals and the first system for automated characterization of piglet group activities, including nursing, sleeping, and active times. PigSense uses physical knowledge of the structural vibration characteristics caused by pig-activity-induced load changes to recognize different behaviors of the sow and piglets. For our system to survive the harsh environment of the farrowing pen for three months, we designed simple, durable sensors for physical fault tolerance, then installed many of them, pooling their data to achieve algorithmic fault tolerance even when some do stop working. The key focus of this work was to create a robust system that can withstand challenging environments, has limited installation and maintenance requirements, and uses domain knowledge to precisely detect a variety of swine activities in noisy conditions while remaining flexible enough to adapt to future activities and applications. We provided an extensive analysis and evaluation of all-round swine activities and scenarios from our one-year field deployment across two pig farms in Thailand and the USA. To help assess the risk of crushing, farrowing sicknesses, and poor maternal behaviors, PigSense achieves an average of 97.8% and 94% for sow posture and motion monitoring, respectively, and an average of 96% and 71% for ingestion and excretion detection. To help farmers monitor piglet feeding, starvation, and illness, PigSense achieves an average of 87.7%, 89.4%, and 81.9% in predicting different levels of nursing, sleeping, and being active, respectively. In addition, we show that our monitoring of signal energy changes allows the prediction of farrowing in advance, as well as status tracking during the farrowing process and on the occasion of farrowing issues. Furthermore, PigSense also predicts the daily pattern and weight gain in the lactation cycle with 89% accuracy, a metric that can be used to monitor the piglets’ growth progress over the lactation cycle.

The rising older adults population has led to an increased demand for in-home health monitoring to support their well-being in daily life. For instance, localization and tracking are essential applications in elderly monitoring since they can provide various information on health, mobility and can detect falls. The required power and computational resources of traditional acoustic sensor-array solutions make them unavailable on power- and computation- constrained embedded devices. In this paper, we present LEVO, a single-point directional acoustic sensing system that leverages simple LEGO® bricks to build up a physical structure that can embed directional information into a signal waveform. Our preliminary results verifies the feasibility of adopting LEVO for signal direction recognition from signal-point sensing data.

We present a passive and non-intrusive sensing system for monitoring hand washing activity using structural vibration sensing. Proper hand washing is one of the most effective ways to limit the spread and transmission of disease, and has been especially critical during the COVID-19 pandemic. Prior approaches include direct observation and sensing-based approaches, but are limited in non-clinical settings due to operational restrictions and privacy concerns in sensitive areas such as restrooms. Our work introduces a new sensing modality for hand washing monitoring, which measures hand washing activity-induced vibration responses of sink structures, and uses those responses to monitor the presence and duration of hand washing. Primary research challenges are that vibration responses are similar for different activities, occur on different surfaces/structures, and tend to overlap/coincide. We overcome these challenges by extracting information about signal periodicity for similar activities through cepstrum-based features, leveraging hierarchical learning to differentiate activities on different surfaces, and denoting “primary/secondary” activities based on their relative frequency and importance. We evaluate our approach using real-world hand washing data across four different sink structures/locations, and achieve an average F1-score for hand washing activities of 0.95, which represents an 8.8X and 10.2X reduction in error over two different baseline approaches.

Automated monitoring of livestock behavior can help farmers economically by detecting changes in animal welfare. Prior approaches use video, which requires light and high storage capability, or motion detection, which has difficulty separating subtle activities. Wearable sensors can address these issues but are vulnerable to destruction by the animals. To the best of our knowledge, we present the first system that uses structural vibration to track animal behavior, and the first system to automatically detect piglet nursing. PigNet uses vibration sensors attached to a pig pen to sense the unique vibration patterns and changes in structural response caused by the animals' movement and position within the pen. Combined with our knowledge of pig behavior, we use this physical knowledge of vibration characteristics to detect pig activities and track piglet growth in a real farm environment. Our system is designed to be robust to the harsh environment, which can create unpredictable noise, as well as physically damage or disconnect sensor nodes. When deployed in a real-world farm environment, our system was able to achieve a daily pen-level status profile of up to 90% accuracy, which tracks nursing activity, sow lying activity, and changes in piglet growth over the weeks-long pre-weaning period.

Automated monitoring of humans and animals can facilitate better health and productivity. Prior monitoring approaches use video, which requires line-of-sight and high processing power, or motion detection, which has difficulty separating subtle activities. Wearable sensors can address these issues but are vulnerable to animal destructiveness and human forgetfulness. We present a system that uses structural vibration to monitor animal or human behavior. We use domain knowledge to adapt this system to different environments, and evaluate it on humans in a home office environment and on pigs at an operational pig farm.

We present Deskbuddy, a vibration-based system that can track a user's activities through their desk. Tracking sitting and other office related activities let us remind the user to have healthier working habits, as well as giving information about how office spaces are used. Many solutions have been proposed for office activity tracking, but they either require the user to wear a device, or they use cameras or microphones, which can make subjects uncomfortable. Our demo includes a small vibration sensor that sits on a table that can detect four office related activities. We capture the signal from the vibration sensor, extract features, and perform classification on the resulting features. The full functionality of the system will be shown in a video. In order for our demo to be more effective in a crowded environment, we have re-trained it to detect only typing versus not typing.

Using geophones to sense bed vibrations caused by ballistic force has shown great potential in monitoring a person's heart rate during sleep. It does not require a special mattress or sheets, and the user is free to move around and change position during sleep. Earlier work has studied how to process the geophone signal to detect heartbeats when a single subject occupies the entire bed. In this study, we develop a system called VitalMon, aiming to monitor a person's respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in amplitude fluctuation of the heartbeat signal.