Jonathon Fagert

FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing

We present FootprintID, an indoor pedestrian identification system that utilizes footstep-induced structural vibration to infer pedestrian identities for enabling various smart building applications. Previous studies have explored other sensing methods, including vision-, RF-, mobile-, and acoustic-based methods. They often require specific sensing conditions, including line-of-sight, high sensor density, and carrying wearable devices. Vibration-based methods, on the other hand, provide easy-to-install sparse sensing and utilize gait to distinguish different individuals. However, the challenge for these methods is that the signals are sensitive to the gait variations caused by different walking speeds and the floor variations caused by structural heterogeneity. We present FootprintID, a vibration-based approach that achieves robust pedestrian identification. The system uses vibration sensors to detect footstep-induced vibrations. It then selects vibration signals and classifiers to accommodate sensing variations, taking step location and frequency into account. We utilize the physical insight on how individual step signal changes with walking speeds and introduce an iterative transductive learning algorithm (ITSVM) to achieve robust classification with limited labeled training data. When trained only on the average walking speed and tested on different walking speeds, FootprintID achieves up to 96% accuracy and a 3X improvement in extreme speeds compared to the Support Vector Machine. Furthermore, it achieves up to 90% accuracy (1.5X improvement) in uncontrolled experiments.

Characterizing left-right gait balance using footstep-induced structural vibrations

In this paper, we introduce a method for estimating human left/right walking gait balance using footstep-induced structural vibrations. Understanding human gait balance is an integral component of assessing gait, neurological and musculoskeletal conditions, overall health status, and risk of falls. Existing techniques utilize pressure- sensing mats, wearable devices, and human observation-based assessment by healthcare providers. These existing methods are collectively limited in their operation and deployment; often requiring dense sensor deployment or direct user interaction. To address these limitations, we utilize footstep-induced structural vibration responses. Based on the physical insight that the vibration energy is a function of the force exerted by a footstep, we calculate the vibration signal energy due to a footstep and use it to estimate the footstep force. By comparing the footstep forces while walking, we determine balance. This approach enables non-intrusive gait balance assessment using sparsely deployed sensors. The primary research challenge is that the floor vibration signal energy is also significantly affected by the distance between the footstep location and the vibration sensor; this function is unclear in real-world scenarios and is a mixed function of wave propagation and structure-dependent properties. We overcome this challenge through footstep localization and incorporating structural factors into an analytical force-energy-distance function. This function is estimated through a nonlinear least squares regression analysis. We evaluate the performance of our method with a real-world deployment in a campus building. Our approach estimates footstep forces with a RMSE of 61.0N (8% of participants body weight), representing a 1.54X improvement over the baseline.

Characterizing Structural Changes to Estimate Walking Gait Balance

We present a method for improving left-right walking gait balance using structural floor vibration sensing by characterizing changes in structural properties in the sensing area. Understanding and measuring human gait balance can be used to assess overall health status, mobility, and rehabilitation progress. The key research challenge is that structural properties in the sensing area may differ from one footstep location to the next, resulting in inaccurate footstep force and balance estimations. To address this challenge, our method performs sensor selection using the insight that some sensors in the sensor network are in a similar structural region as the footstep location and, therefore, are not as effected by the observed variations in structural properties as the other sensors. We evaluate the performance of our method by conducting uncontrolled real-world walking experiments in a residential structure. This evaluation shows that our method achieves a 1.6X reduction in force estimation error and a 2.4X reduction in balance estimation as compared to the baseline approach.

Occupant-induced office floor vibration dataset for activity level monitoring

Occupant activity level plays an important part in many smart building applications, such as power management [5] and elderly care [1]. There are several approaches to obtain occupant states, including visual-, acoustic-, and radio frequency based techniques. However, visual-based approaches require light-of-sight, while acoustic-based approaches are sensitive to background noise. On the other hand, radio-based approaches often require the occupant to carry a particular device to receive signals from base stations, which requires direct participation from the monitored occupants at all times.

Seat vibration for heart monitoring in a moving automobile: extended abstract

Continuous heart rate monitoring in cars can allow ambient health monitoring and help track driver stress and fatigue. We present a data set from accelerometers embedded in a car seat which includes ten people sitting in the passenger seat of a moving car, and surface ECG data of each user to provide ground truth of the heartbeat. This data can be used to analyze the heart activity of people in cars, despite the presence of high levels of noise from car motion, human motion, and the car engine.