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Passive inhome estimation of gait velocity from motion sensors, arXiv preprint arXiv:1310.4880
"... Abstract—Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in elderly populations. Gait velocity is often assessed clinically, but clinical assessments occur infrequently and thus do not allow optimal detection of key health changes whe ..."
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Abstract—Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in elderly populations. Gait velocity is often assessed clinically, but clinical assessments occur infrequently and thus do not allow optimal detection of key health changes when they occur. In this paper, we propose using the time it takes a person to move between rooms in their home referred to as “transition times” estimated from passive infrared motion sensors installed in a patients own home to predict gait velocity. By using a support vector regression approach to model the relationship between transition times and observed gait velocities, we show that we can predict unobserved velocities accurately. In particular, we demonstrate that the proposed approach has an average error of 4 cm/sec using data collected over a 5 year period from 76 study participants monitored both in their own homes and within a clinical setting. The proposed method is simple and cost effective, and has advantages over competing approaches such as not requiring patients to wear a device or needing dedicated sensors to measure gait. Furthermore, this method provides substantially more frequent estimates of gait velocity than are provided by other approaches. Index Terms—Unobtrusive monitoring, ubiquitous computing, gait, walking speed, passive infrared (PIR) motion sensors I.
An Overview of Moving Object Trajectory Compression Algorithms
"... Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential in ..."
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Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining.
1Affect Sensing on Smartphone Possibilities of Understanding Cognitive Decline in Ageing Population.
"... Abstract—Due to increasing sensing capacity, smartphones offer unprecedented opportunity to monitor human health. Affect sensing is one such essential monitoring that can be achieved on smartphones. Information about affect can be useful for many modern applications. In particular, it can be potenti ..."
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Abstract—Due to increasing sensing capacity, smartphones offer unprecedented opportunity to monitor human health. Affect sensing is one such essential monitoring that can be achieved on smartphones. Information about affect can be useful for many modern applications. In particular, it can be potentially used for understanding cognitive decline in aging population. In this paper we present an overview of the existing literature that offer affect sensing on smartphone platform. Most importantly, we present the challenges that need to be addressed to make affect sensing on smartphone a reality. I.
Signal Reconstruction from Rechargeable Wireless Sensor Networks using Sparse Random Projections
"... Abstract—Due to nonhomogeneous spread of sunlight, sensing nodes possess nonuniform energy budget in rechargeable Wireless Sensor Networks (WSNs). An energyaware workload distribution strategy is therefore necessary to achieve good data accuracy subject to energyneutral operation. Our previous ..."
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Abstract—Due to nonhomogeneous spread of sunlight, sensing nodes possess nonuniform energy budget in rechargeable Wireless Sensor Networks (WSNs). An energyaware workload distribution strategy is therefore necessary to achieve good data accuracy subject to energyneutral operation. Our previously proposed Energy Aware Sparse approximation Technique (EAST) can approximate a signal, by adapting sensor node sampling workload according to solar energy availability. However, the major shortcoming of EAST is that it does not guarantee an optimal sensing strategy. In other words EAST offers energy neutral operation, however it does not offer the best utilization of sensor node energy, which compromises the reconstruction accuracy. In order to overcome this shortcoming, we propose EAST+ which, maximizes the reconstruction accuracy subject to energy neutral operations. We also propose a distributed algorithm for EAST+, which offers accurate signal reconstruction with limited node tobase communications.
Continuous Gait Velocity Estimation using Houseohld Motion Detectors
"... Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. Gait velocity is often assessed clinically, but the assessments occur infrequently and thus do not allow optimal detection of key health changes when they occur. In thi ..."
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Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. Gait velocity is often assessed clinically, but the assessments occur infrequently and thus do not allow optimal detection of key health changes when they occur. In this paper, we show the time it takes a person to move between rooms in their home denoted “transition times ” can predict gait velocity when estimated from passive infrared motion detectors installed in a patient’s own home. Using a support vector regression approach to model the relationship between transition times and gait velocities, we show that velocity can be predicted with an average error less than 2.5 cm/sec. This is demonstrated with data collected over a 5 year period from 74 older adults monitored in their own homes. This method is simple and cost effective, and has advantages over competing approaches such as: obtaining 20100x more gait velocity measurements per day, and offering the fusion of locationspecific information with time stamped gait estimates. These advantages allow stable estimates of gait parameters (maximum or average speed, variability) at shorter time scales than current approaches. This also provides ∗Corresponding author
Novel Methods for Activity Classification and Occupany Prediction Enabling Finegrained HVAC Control.
"... Much of the energy consumption in buildings is due to HVAC systems, which has motivated several recent studies on making these systems more energyefficient. Occupancy and activity are two important aspects, which need to be correctly estimated for optimal HVAC control. However, stateoftheart met ..."
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Much of the energy consumption in buildings is due to HVAC systems, which has motivated several recent studies on making these systems more energyefficient. Occupancy and activity are two important aspects, which need to be correctly estimated for optimal HVAC control. However, stateoftheart methods to estimate occupancy and classify activity require infrastructure and/or wearable sensors which suffers from lower acceptability due to higher cost. Encouragingly, with the advancement of the smartphones, these are becoming more achievable. Most of the existing occupancy estimation techniques have the underlying assumption that the phone is always carried by its user. However, phones are often left at desk while attending meeting or other events, which generates estimation error for the existing phone based occupancy algorithms. Similarly, in the recent days the emerging theory of Sparse Random Classifier (SRC) has been applied for activity classification on smartphone, however, there are rooms to improve the onphone processing. We propose a novel sensor fusion method which offers almost 100% accuracy for occupancy estimation. We also propose an activity classification algorithm, which offers similar accuracy as of the stateoftheart SRC algorithms while offering 50 % reduction in processing.
A Deterministic Construction of Projection matrix for Adaptive Trajectory Compression
"... Abstract—Compressive Sensing, which offers exact reconstruction of sparse signal from a small number of measurements, has tremendous potential for trajectory compression. In order to optimize the compression, trajectory compression algorithms need to adapt compression ratio subject to the compressib ..."
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Abstract—Compressive Sensing, which offers exact reconstruction of sparse signal from a small number of measurements, has tremendous potential for trajectory compression. In order to optimize the compression, trajectory compression algorithms need to adapt compression ratio subject to the compressibility of the trajectory. Intuitively, the trajectory of an object moving in starlight road is more compressible compared to the trajectory of a object moving in winding roads, therefore, higher compression is achievable in the former case compared to the later. We propose an insitu compression technique underpinning the support vector regression theory, which accurately predicts the compressibility of a trajectory given the mean speed of the object and then apply compressive sensing to adapt the compression to the compressibility of the trajectory. The conventional encoding and decoding process of compressive sensing uses predefined dictionary and measurement (or projection) matrix pairs. However, the selection of an optimal pair is nontrivial and exhaustive, and random selection of a pair does not guarantee the best compression performance. In this paper, we propose a deterministic and data driven construction for the projection matrix which is obtained by applying singular value decomposition to a sparsifying dictionary learned from the dataset. We analyze case studies of pedestrian and animal trajectory datasets including GPS trajectory data from 127 subjects. The experimental results suggest that the proposed adaptive compression algorithm, incorporating the deterministic construction of projection matrix, offers significantly better compression performance compared to the stateoftheart alternatives. Index Terms—Trajectory compression, compressive sensing, sparse coding, singular value decomposition. F 1
IEEE SENSORS JOURNAL 1 SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks
"... Abstract—Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to com ..."
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Abstract—Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using 6 datasets shows that our proposed algorithm can achieve submetre accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a stateoftheart trajectory compression algorithm show that our algorithm can reduce the error by 1060 cm for the same compression ratio. Index Terms—Mobile sensor networks; trajectory compression; compressive sensing; adaptive compression; support vector regression; sparse coding; singular value decomposition. I.