Activity and environment classification using foot mounted navigation sensors

Bancroft, Jared B.; Garrett, David

doi:10.1109/ipin.2012.6418902

Cited by 19 publications

(14 citation statements)

References 4 publications

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“…The term ω 3,3 at row, column (3,3) in the matrix expresses the inverse covariance of the orientation angle constraint. In the experiments reported in this paper, we set Ω to be equal to the identity matrix with the exception of the term ω 3,3 which was equal to 4 (motion edges and loop-closure edges) or to 0 when the angle constraint was unknown (landmark edges).…”

Section: Graphslammentioning

confidence: 99%

“…Note that foot-mounted IMUs [3], [38] with the Zero Velocity Update Method typically achieve a far better pedestrian dead reckoning accuracy, because of both better hardware and reduced noise due, as the IMU is attached to the foot that hits the ground as opposed to being stashed loose in a pocket, but they lack the convenience of consumer-grade smartphones casually worn in the pocket.…”

Section: )mentioning

confidence: 99%

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SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

Mirowski¹,

Ho²,

Yi³

et al. 2013

International Conference on Indoor Positioning and Indoor Navigation

146

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Abstract-Indoor localization typically relies on measuring a collection of RF signals, such as Received Signal Strength (RSS) from WiFi, in conjunction with spatial maps of signal fingerprints. A new technology for localization could arise with the use of 4G LTE telephony small cells, with limited range but with rich signal strength information, namely Reference Signal Received Power (RSRP). In this paper, we propose to combine an ensemble of available sources of RF signals to build multi-modal signal maps that can be used for localization or for network deployment optimization. We primarily rely on Simultaneous Localization and Mapping (SLAM), which provides a solution to the challenge of building a map of observations without knowing the location of the observer. SLAM has recently been extended to incorporate signal strength from WiFi in the so-called WiFi-SLAM. In parallel to WiFi-SLAM, other localization algorithms have been developed that exploit the inertial motion sensors and a known map of either WiFi RSS or of magnetic field magnitude. In our study, we use all the measurements that can be acquired by an off-the-shelf smartphone and crowd-source the data collection from several experimenters walking freely through a building, collecting time-stamped WiFi and Bluetooth RSS, 4G LTE RSRP, magnetic field magnitude, GPS reference points when outdoors, Near-Field Communication (NFC) readings at specific landmarks and pedestrian dead reckoning based on inertial data. We resolve the location of all the users using a modified version of Graph-SLAM optimization of the users poses with a collection of absolute location and pairwise constraints that incorporates multi-modal signal similarity. We demonstrate that we can recover the user positions and thus simultaneously generate dense signal maps for each WiFi access point and 4G LTE small cell, "from the pocket". Finally, we demonstrate the localization performance using selected single modalities, such as only WiFi and the WiFi signal maps that we generated.

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Section: Graphslammentioning

confidence: 99%

Section: )mentioning

confidence: 99%

SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

Mirowski¹,

Ho²,

Yi³

et al. 2013

International Conference on Indoor Positioning and Indoor Navigation

146

View full text Add to dashboard Cite

show abstract

“…Reddy et al [16] used a mobile phone with a GPS receiver and an accelerometer to determine transportation modes of users, achieving 93.6% classification accuracy with the use of discrete Hidden Markov Models. Bancroft et al [17] used a footmounted device (with GPS receiver and inertial measurement unit) to determine different motion-related activities, such as walking, running, biking, and moving in a vehicle, reporting less than 1% classification error during 99% of measurements. Jin et al [18] detected several motion states using an armbandmounted accelerometer and a "fuzzy inference system," which can marginally be considered as a Machine Learning technique.…”

Section: Related Research Workmentioning

confidence: 99%

Development of a contextual thinking engine in mobile devices

Chen

Chu

Liu

et al. 2014

2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS)

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This paper introduces a framework of contextual thinking in mobile devices. It is based on real-time sensing of local time, significant locations, location-dwelling states, and user states to infer significant activities. A significant activity is a well-defined activity to be inferred, for example, waiting for a bus, having a meeting, working in office, taking a break in a coffee shop et al. A significant location is defined as a geofence, which can be a node associated with a circle, or a polygon. A location-dwelling state is defined as enter into a significant location, the location-dwelling duration, or exit from a significant location. A user state is a combination of user mobility states, user actions, user social states and event psychological states. With this initial study, we just focus on the user motion states including static, slow walking, walking and fast moving that can be fast walking or driving. However, the framework and the activity inference algorithm are flexible for adopting other user states in the future. Using the measurements of the built-in sensors and radio signals in mobile devices, we can capture a snapshot of a contextual tuple for every second, which includes a time tag, an ID of a significant location, a locationdwelling duration, and a user state. The sequence of contextual tuples is used as the inputs for inferring the user significant activities. The contextual thinking engine will evaluate the posteriori probability of each significant activity for each given contextual tuple using a Bayesian approach. An "un-defined" activity is adopted to cover all activities other than the selected significant activities. A prototype of the contextual thinking engine has been developed in the Geospatial Computing Lab at Texas A&M University Corpus Christi. A test environment was setup on the campus. Six significant activities were defined and tested by two different testers for three days using two different smartphones. These significant activities include: 1) working in an office; 2) having a meeting; 3) having a lunch, 4) having a coffee break, 5) visiting the library, and 6) waiting for a bus. An "un-defined" activity was included to cover all activities other than the selected significant activities. The inferred activities were then compared with the labeled activities to assess the performance of the contextual thinking engine. We demonstrated that the success rate of inference was more than 90% on average. We recognized that the positioning accuracy plays a significant role in the inference algorithm because it has direct impact to two elements in the contextual tuple: the significant location and the location-dwelling duration.

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“…Originally developed in the medical context, gait signal processing and analysis has been extended to security and navigation applications. Globally, motion recognition techniques are applied to inertial signals recorded with body fixed sensors for inferring the repetitive gait patterns [7,8].…”

Section: Gait Analysismentioning

confidence: 99%

“…Furthermore the torso oscillations occurring during normal walking gait [19] will not be reflected by the heading change sensed by the handheld GNSS receiver but will impact the PDR implementation. To cope with the imperfection in sensing the misalignment between the hand's and pedestrian's headings, white process noise is added to the system (7). The fact that the heading sensed by Doppler data through the receiver velocity vector, is also affected by this discrepancy further justifies the additional process noise contributions.…”

Section: A) Doppler Measurementsmentioning

confidence: 99%

Adaptative pedestrian displacement estimation with a smartphone

Renaudin¹,

Demeule²,

Ortiz³

2013

International Conference on Indoor Positioning and Indoor Navigation

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Pedestrian dead reckoning is one of the most promising processing strategies of inertial signals collected with a smartphone for autonomous indoor personal navigation. When the sensors are held in hand, step length models are usually used to estimate the walking distance. They combine stride frequency with a finite number of physiological and descriptive parameters that are calibrated with training data for each person. But even under steady conditions, several physiological conditions are impacting the walking gait and consequently these parameters. Frequent calibration is needed to tune these models prior to relying on free inertial navigation solutions in indoor locations. Two hybridization filters are proposed for calibrating the step length model and estimating the navigation solution. They integrate either GNSS standalone positions or GNSS Doppler depending on the coupling level. A data collection performed with four test subjects show the variations of these parameters for the same person during his journey and effectiveness of the calibration for improving the estimation of walking distances.Thanks to the new filters, the error on the travelled distance gets reduced to 7% with the loosely coupled filter and 2% with the tightly coupled filter.

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Activity and environment classification using foot mounted navigation sensors

Cited by 19 publications

References 4 publications

SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

Development of a contextual thinking engine in mobile devices

Adaptative pedestrian displacement estimation with a smartphone

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