Footstep-induced Floor Vibrations Research Articles

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Recursive Sparse Representation for Identifying Multiple Concurrent Occupants Using Floor Vibration Sensing

In this paper, we present a multiple concurrent occupant identification approach through footstep-induced floor vibration sensing. Identification of human occupants is useful in a variety of indoor smart structure scenarios, with applications in building security, space allocation, and healthcare. Existing approaches leverage sensing modalities such as vision, acoustic, RF, and wearables, but are limited due to deployment constraints such as line-of-sight requirements, sensitivity to noise, dense sensor deployment, and requiring each walker to wear/carry a device. To overcome these restrictions, we use footstep-induced structural vibration sensing. Footstep-induced signals contain information about the occupants' unique gait characteristics, and propagate through the structural medium, which enables sparse and passive identification of indoor occupants. The primary research challenge is that multiple-person footstep-induced vibration responses are a mixture of structurally-codependent overlapping individual responses with unknown timing, spectral content, and mixing ratios. As such, it is difficult to determine which part of the signal corresponds to each occupant. We overcome this challenge through a recursive sparse representation approach based on cosine distance that identifies each occupant in a footstep event in the order that their signals are generated, reconstructs their portion of the signal, and removes it from the mixed response. By leveraging sparse representation, our approach can simultaneously identify and separate mixed/overlapping responses, and the use of the cosine distance error function reduces the influence of structural codependency on the multiple walkers' signals. In this way, we isolate and identify each of the multiple occupants' footstep responses. We evaluate our approach by conducting real-world walking experiments with three concurrent walkers and achieve an average F1 score for identifying all persons of 0.89 (1.3x baseline improvement), and with a 10-person "hybrid" dataset (simulated combination of single-walker real-world data), we identify 2, 3, and 4 concurrent walkers with a trace-level accuracy of 100%, 93%, and 73%, respectively, and observe as much as a 2.9x error reduction over a naive baseline approach.

A framework for occupancy detection and tracking using floor-vibration signals

In sensed buildings, information related to occupant movement helps optimize important functionalities such as caregiving, energy management, and security enhancement. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. These systems compromise the privacy of building occupants and may affect their behavior. Occupant detection and tracking using floor-vibration measurements that are induced by footsteps is a non-intrusive and inexpensive sensing method. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. In addition, spurious events such as door closing and falling objects may produce vibrations that are similar to footstep impacts. These events have to be detected and disregarded. Tracking occupants is complicated due to uncertainties associated with walking styles, walking speed, shoe type, health, and mood. Also, spatial variation in structural behavior of floor slabs adds ambiguity to the task of occupant tracking, which cannot be addressed using data-driven strategies alone. In this paper, a framework for occupant detection and tracking is developed. Occupant detection is carried out based on signal information. This method outperforms existing threshold-based methods. Support-vector-machine classifiers, trained with time and frequency-domain features, successfully distinguish footsteps from spurious events and determine the number of occupants walking simultaneously. A model-based data-interpretation approach is used for occupant tracking. Structural-mechanics models are used to identify a population of possible occupant locations and trajectories. Up to two occupants can be tracked by accommodating systematic bias and uncertainties from sources such as modeling assumptions and variability in walking gaits. A hybrid framework for occupant detection and tracking that combines model-free approaches for occupancy detection with structural behavior models for tracking is developed and tested on two full-scale case studies. These studies successfully validate the utility of the framework for buildings having sparse sensor configurations that measure floor vibrations.

Using footstep-induced vibrations for occupant detection and recognition in buildings

Occupant detection and recognition support functional goals such as security, healthcare, and energy management in buildings. Typical sensing approaches, such as smartphones and cameras, undermine the privacy of building occupants and inherently affect their behavior. To overcome these drawbacks, a non-intrusive technique using floor-vibration measurements, induced by human footsteps, is outlined. Detection of human-footstep impacts is an essential step to estimate the number of occupants, recognize their identities and provide an estimate of their probable locations. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. Also, signals from multiple occupants walking simultaneously overlap, which may lead to inaccurate event separation. Signals corresponding to events, once extracted, can be used to identify the number of occupants and their locations. Spurious events such as door closing, chair dragging and falling objects may produce vibrations similar to footstep-impacts. Signals from such spurious events have to be discarded as outliers to prevent inaccurate interpretations of floor vibrations for occupant detection. Walking styles differ among occupants due to their anatomies, walking speed, shoe type, health and mood. Thus, footstep-impact vibrations from the same person may vary significantly, which adds uncertainty and complicates occupant recognition. In this paper, efficient strategies for event-detection and event-signal extraction have been described. These strategies are based on variations in standard deviations over time of measured signals (using a moving window) that have been filtered to contain only low-frequency components. Methods described in this paper for event detection and event-signal extraction perform better than existing threshold-based methods (fewer false positives and false negatives). Support vector machine classifiers are used successfully to distinguish footsteps from other events and to determine the number of occupants on a floor. Convolutional neural networks help recognize the identity of occupants using footstep-induced floor vibrations. The utility of these strategies for footstep-event detection, occupant counting, and recognition is validated successfully using two full-scale case studies.

Obstruction-invariant occupant localization using footstep-induced structural vibrations

In this paper, we characterize the effects of obstructions on footstep-induced floor vibrations to enable obstruction-invariant indoor occupant localization. Occupant localization is important in smart building applications such as smart healthcare and energy management. Maintenance and installment requirements limit the application of current sensing approaches (e.g., mobile-based, RF-based, and pressure-based sensing) in real-life applications. To overcome these limitations, prior work has utilized footstep-induced structural vibrations for occupant localization. The main intuition behind these approaches is that the footstep-induced floor vibration waves take different amounts of time to arrive at different sensors. These Time-Differences-of-Arrival (TDoA) can then be leveraged to locate the footstep by assuming similar velocities between the footstep and various sensor locations. This assumption makes these approaches suitable for open areas; however, real buildings have various types of obstructions (e.g., walls, furniture, etc.) which affect wave propagation velocities and hence significantly reduce localization accuracy. Therefore, the prior work requires unobstructed paths between footsteps and sensors for accurate occupant localization, which increases the sensing density requirement and thus, instrumentation and maintenance costs. We have observed that the obstruction mass is one of the key factors in affecting the wave propagation velocity and reducing the localization accuracy. Therefore, to overcome the obstruction challenge, we localize footsteps by considering different velocities between the footsteps and sensors depending on the existence and mass of obstruction on the wave path. Specifically, we (1) detect and estimate the mass of the obstruction by characterizing the wave attenuation rate, (2) use this estimated mass to find the propagation velocities for localization by modeling the velocity-mass relationship through the lamb wave characteristics, and (3) introduce a non-isotropic multilateration approach which robustly leverages these propagation velocities to locate the footsteps (and the occupants). In field experiments, we achieved average localization error of 0.61 meters, which is (1) the same as the average localization error when there is no obstruction and (2) 1.6X improvement compared to the baseline approach.

Structure- and Sampling-Adaptive Gait Balance Symmetry Estimation Using Footstep-Induced Structural Floor Vibrations

This paper presents a structure- and sampling-adaptive approach for analyzing human footstep-induced structural floor vibrations to estimate footstep ground reaction forces (GRFs) and gait balance symmetry. Balance symmetry and footstep GRFs are critical indicators of overall gait health and elderly fall risks. Prior works, including direct observation by trained medical personnel, computer vision-, pressure sensor-, and wearable-based sensing, are limited due to operational restrictions. We introduce a nonintrusive balance symmetry monitoring approach, which utilizes sparse structural vibration sensing. The intuition is that footstep-induced floor vibration responses are proportional to footstep GRFs, and balance symmetry can be defined using consecutive GRF pairs. However, GRF-vibration relationships are also influenced by spatially-varying structural properties and gait sampling bias, introducing errors to real-world estimations. We address these challenges first by extracting structural regions to overcome spatially-varying vibration behavior and then by developing a kernel-based robust regression model to overcome biased training data and enable robust GRF and balance symmetry modeling. We evaluate our approach through real-world experiments, achieving a balance symmetry index estimation accuracy as high as 96.5%.

Step-Level Occupant Detection across Different Structures through Footstep-Induced Floor Vibration Using Model Transfer

AbstractThis paper presents a floor-vibration-based step-level occupant-detection approach that enables detection across different structures through model transfer. Detecting the occupants through...

Model-Based Occupant Tracking Using Slab-Vibration Measurements

Sensor-based occupant tracking has the potential to enhance knowledge of the utilization of buildings. Occupancy-tracking strategies using footstep-induced floor vibrations may be beneficial for thermal-load prediction, security enhancement and care-giving without undermining privacy. Current floor-vibration-based occupant-tracking methodologies are based on data-driven techniques that do not include a physics-based model of the structural behavior of the floor slab. These techniques suffer from ambiguous interpretations when signals are affected by complex configurations of structural and non-structural elements such as beams and walls. Using a physics-based model for data-interpretation enables deployment of sparse number of sensors in contexts of non-uniform structural configurations. In this paper, an application of physics-based data interpretation using error-domain model falsification (EDMF) is presented to track an occupant within an office environment through footstep-induced floor vibrations. EDMF is a population-based approach that incorporates various sources of uncertainty, including bias, arising from measurements and modeling. EDMF involves the rejection of simulated model responses that contradict footstep-induced floor vibration measurements. Thus, EDMF provides a set of candidate locations from an initial population of possible occupant locations. A sequential analysis that accommodates information from previous footsteps is then used to enhance candidate locations and identify trajectories among candidates. In this way, incorporating structural behavior in interpreting vibration measurements induced by occupant footsteps has the potential to identify accurately the trajectory of an occupant in buildings with complex configurations, thereby providing tracking information without undermining privacy.

Occupant localization using footstep-induced structural vibration

Abstract In this paper, we present an occupant localization approach through sensing footstep-induced floor vibrations. Occupant location information is an important part of many smart building applications, such as energy and space management in a personal home or patient tracking in a hospital room. Adoption of current occupant location sensing approaches in smart buildings (e.g., camera, radio frequency (RF), mobile devices, etc.) is often limited due to the maintenance, installment, and calibration requirements of these sensing systems. To overcome these limitations, we introduce a new approach to use footstep-induced structural vibration for step-level occupant localization. The intuition behind this approach is that footsteps induce floor vibrations which are received in different vibration sensor locations at different times. This paper focuses on localizing a single occupant within each sensing range. To localize the footsteps, we utilize the time differences of arrival (TDoA) of the footstep-induced vibrations. However, this approach involves two main challenges: (1) the vibration wave propagation in the floor is of dispersive nature (i.e., different frequency components travel at different velocities) and (2) due to floor heterogeneity, these wave propagation velocities vary in different structures as well as in different locations in a structure. These issues lead to large localization inaccuracies or calibration requirements. To address dispersion challenge, we present a decomposition-based dispersion mitigation technique which extracts specific components (which have similar propagation characteristics) for localization. To address velocity variations, we introduce an adaptive multilateration approach that employs heuristics to constrain the search space and robustly locate the footsteps when the propagation velocity is unknown. Constraining the search space overcomes the additional complexity which is resulted from adding an unknown variable (propagation velocity). We evaluated our approach using the field experiments in 3 different types of buildings (both commercial and residential) with human participants. The results show an average localization error of 0.34 m, which corresponds to a 6X reduction in error compared to a baseline method. Furthermore, our approach resulted in standard deviation of as low as 0.18 m, which corresponds to a 8.7X improvement in precision compared to the baseline approach.