Emotion recognition is critical for various applications, including the early detection of mental health disorders and emotion-based smart home systems. Previous studies utilized various sensing methods for emotion recognition, such as wearable sensors, cameras, and microphones. However, these methods are often intrusive or raise significant privacy concerns, which may reduce user acceptance for continuous, long-term deployment. This paper introduces a non-intrusive and privacy-friendly personalized emotion recognition system, EmotionVibe, which leverages footstep-induced floor vibrations for emotion recognition. The main idea of EmotionVibe is that individuals' emotional states influence their gait patterns, subsequently affecting the floor vibrations induced by their footsteps. However, there are two main research challenges: 1) the complex and indirect relationship between human emotions and footstep-induced floor vibrations and 2) the large between-person variations within the relationship between emotions and gait patterns. To address these challenges, we first empirically characterize this complex relationship and develop an emotion-sensitive feature set including gait-related and vibration-related features from footstep-induced floor vibrations. Furthermore, we personalize the emotion recognition system for each user by calculating gait similarities between the target person (i.e., the person whose emotions we aim to recognize) and those in the training dataset and assigning greater weights to training people with similar gait patterns in the loss function. We evaluated our system in human walking experiments with 20 participants, summing up to 37,001 footstep samples. EmotionVibe achieved the mean absolute error (MAE) of 1.11 and 1.07 for valence (unpleasant to pleasant) and arousal (calm to excited) score estimations, respectively, reflecting 19.0% and 25.7% error reduction compared to the baseline method (using only gait-related features without personalization).

Crowd monitoring in sports stadiums is important to enhance public safety and improve audience experience. Existing approaches mainly rely on cameras and microphones, which can cause significant disturbances and often raise privacy concerns. In this paper, we sense floor vibration, which provides a less disruptive and more non-intrusive way of crowd sensing, to predict crowd behavior. However, since the vibration-based crowd monitoring approach is newly developed, one main challenge is the lack of training data due to sports stadiums are usually large public spaces with complex physical activities.

This paper addresses challenges in monitoring human behavior in crowds through floor vibration sensing, overcoming limitations like subjective manual observation, visual occlusions, and audio interference. Our approach involves tackling limited-data vibration signal tasks by conducting pre-training across modalities, leveraging publicly available audio datasets. By leveraging self-supervised representation learning to pre-train on publicly available audio datasets, our approach reduces data requirements, improves robustness, and minimizes the need for human labeling efforts. Evaluation using in-game stadium vibration data with YouTube audio dataset demonstrates up to 5.8 × error reduction for crowd behavior.

Computer vision has been used more ubiquitously in recent years to understand and measure the environment around us, particularly in our neighborhoods. However, many city-wide sensing applications using vision require large labeling efforts, making various applications difficult on a wide scale. We propose a framework for labeling and self-training of in-car video to detect trash on the roads. Our approach requires minimal manual labeling to identify items not meant to be in the street, sidewalk, or public places, from a front-viewing car camera. Our system provides each frame of a video with a score indicating the amount of trash. To prevent overfitting, due to minimal available data, we remove data with high certainty of trash from the training dataset. The results show that our prediction with manually labeled ground truth yield an R2 of 0.66.

Detecting gait abnormalities is crucial for assessing fall risks and early identification of neuromusculoskeletal disorders such as Parkinson's and stroke. Traditional assessments in gait clinics are infrequent and pose barriers, particularly for disadvantaged populations. Previous efforts have explored sensor-based approaches for in-home gait assessments, yet they face limitations such as visual obstructions (cameras), limited coverage (pressure mats), and the need for device carrying (wearables and insoles). To overcome these limitations, we introduce an in-home gait abnormality detection system using footstep-induced floor vibrations, enabling low-cost, non-intrusive, device-free gait health monitoring. The main research challenge is the high uncertainty in floor vibrations due to gait variations among people, making it challenging to develop a generalizable model for new patients. To address this, we analyze time-frequency-domain features of floor vibration data during specific gait phases and develop a feature transformation method through contrastive learning to address the between-people gait variation challenge. Our method transforms the features from vibrations to an embedding space where samples from different people stay close to each other (robust to people variation) while normal and abnormal gait samples are far apart (sensitive to gait abnormalities). Then, gait abnormalities are detected by a downstream classifier after feature transformation. We evaluated our approach through a real-world walking experiment with 21 participants and achieved an 85% to 95% mean accuracy in detecting various gait abnormalities. This novel method overcomes prior limitations in in-home gait assessments, offering accessible gait abnormality detection without the need for intrusive devices or labels for new patients.

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.

Heart rate is a critical metric for human cardiovascular health. Most common methods for measuring human heart rate involve wearable devices (e.g., electrocardiography, smart watches). However, such devices can cause discomfort to some patients, especially the elderly or young children. This paper presents FreePulse, a heart rate monitoring system for seated subjects using ambient vibrations. FreePulse builds on our past work using vibrations in the building structures around us to measure human activities and health. As people’s hearts beat, they push on the surfaces the body is touching, creating vibrations in those structures. We combine structure response characterization with human pulse modelling to identify these pulse-induced vibrations from ambient vibration. In testing, FreePulse has shown up to 96% pulse rate accuracy on average, competitive with consumer-grade wearable devices.

Pig vital sign monitoring (e.g., estimating the heart rate (HR) and respiratory rate (RR)) is essential to understand the stress level of the sow and detect the onset of parturition. It helps to maximize peri-natal survival and improve animal well-being in swine production. The existing approach mainly relies on manual measurement, which is labor-intensive and only provides a few points of information. Other sensing modalities such as wearables and cameras are developed to enable more continuous measurement, but are still limited due to animal discomfort, data transfer, and storage challenges. In this paper, we introduce PigV2, the first system to monitor pig heart rate and respiratory rate through ground vibrations. Our approach leverages the insight that both heartbeat and respiration generate ground vibrations when the sow is lying on the floor. We infer vital information by sensing and analyzing these vibrations. The main challenge in developing PigV2 is the overlap of vital- and non-vital-related information in the vibration signals, including pig movements, pig postures, pig-to-sensor distances, and so on. To address this issue, we first characterize their effects, extract their current status, and then reduce their impact by adaptively interpolating vital rates over multiple sensors. PigV2 is evaluated through a real-world deployment with 30 pigs. It has 3.4% and 8.3% average errors in monitoring the HR and RR of the sows, respectively.

In-home gait analysis is important for providing early diagnosis and adaptive treatments for individuals with gait disorders. Existing systems include wearables and pressure mats, but they have limited scalability due to dense deployment and device carrying/charging requirements. Recently, vision-based systems have been developed to enable scalable, accurate in-home gait analysis, but it faces privacy concerns due to the exposure of people's appearances and daily activities. To overcome these limitations, our prior work developed footstep-induced structural vibration sensing for in-home gait monitoring, which is device-free, wide-ranged, and perceived as more privacy-friendly. Although it has succeeded in temporal parameter estimation, it shows limited performance for spatial gait parameter estimation due to the low accuracy in footstep localization. In particular, the localization error mainly comes from the estimation error of the wave arrival time at the vibration sensors and its error propagation to wave velocity estimations. To this end, we present GaitVibe+, a vibration-based footstep localization method fused with temporarily installed cameras for in-home gait analysis. Our method has two stages: fusion and operating stages. In the fusion stage, both cameras and vibration sensors are installed to record only a few trials of the subject's footstep data, through which we characterize the uncertainty in wave arrival time and model the wave velocity profiles for the given structure. In the operating stage, we remove the camera to preserve privacy at home. The footstep localization is conducted by estimating the time difference of arrival (TDoA) over multiple vibration sensors, whose accuracy is improved through the reduced uncertainty and velocity modeling during the fusion stage. We evaluate GaitVibe+ through a real-world experiment with 50 walking trials. With only 3 trials of multi-modal fusion, our approach has an average localization error of 0.22 meters, which reduces the spatial gait parameter error by 4.1x (from 111.4% to 27.1%) compared to the existing work.

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.

Detecting a previously unknown person (newcomer detection) is critical for visitor management, intruder prevention, and access control in smart buildings. Biometrics have been used to detect newcomers, including face, fingerprint, voice, iris, etc. These approaches often require active participation of the users or require dense instrumentation. Prior work using footstep-induced floor vibration to identify people removes these requirements, but only functions for known people due to the high variability in footstep-induced vibrations and the limited number of predicted classes in supervised learning. To overcome the limitations, we introduce a newcomer detection system based on non-parametric Bayesian learning, which models the variability and distribution of consecutive footstep-induced floor vibration for newcomer walking patterns. Preliminary results from real-world experiments with 6 people show up to 92% accuracy in newcomer detection with an average of 4 consecutive footsteps.

Monitoring the compliance of social distancing is critical for schools and offices to recover in-person operations in indoor spaces from the COVID-19 pandemic. Existing systems focus on vision- and wearable-based sensing approaches, which require direct line-of-sight or device-carrying and may also raise privacy concerns. To overcome these limitations, we introduce a new monitoring system for social distancing compliance based on footstep-induced floor vibration sensing. This system is device-free, non-intrusive, and perceived as more privacy-friendly. Our system leverages the insight that footsteps closer to the sensors generate vibration signals with larger amplitudes. The system first estimates the location of each person relative to the sensors based on signal energy and then infers the distance between two people. We evaluated the system through a real-world experiment with 8 people, and the system achieves an average accuracy of 97.8% for walking scenario classification and 80.4% in social distancing violation detection.