Localization of Humans, Objects, and Robots Interacting on Load-Sensing Floors

Andries, Mihai; Simonin, Olivier; Charpillet, François

doi:10.1109/jsen.2015.2493122

Cited by 41 publications

(20 citation statements)

References 48 publications

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“…In this case, COP is retrieved using the weighted average of the pressure sensor outputs. The gait of several persons can be simultaneously recorded on the same sensitive area, provided a preprocessing algorithm separates individual footprints [66]. The drawback of the pressure sensor grid is a complicated technical setup that generates a large quantity of raw data.…”

Section: Discussionmentioning

confidence: 99%

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Terrier

2020

Applied Sciences

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Fig. 6. CAM analysis. Twenty-four segments were randomly selected from the training set containing the gait data of six participants (left columns). These segments were used to fine-tune the last layers of the convolutional neural network (CNN) that was pre-trained on the training set of the 30 other participants. This CNN classified 30 segments drawn randomly from the test set with 100% accuracy (right columns). Class activation mapping (CAM) was performed on each sample of the test set. Color coding shows which parts of the signals are prioritized to be used by the CNN to perform classification. Warm color (red, orange): high focus; cold colors (green, blue): low focus. AbstractThe fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty. Thirty-six adults walked on a treadmill equipped with a force platform that recorded the positions of the center of pressure. The raw two-dimensional signals were sliced into segments of two gait cycles . A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2,250 segments with 99.9% overall accuracy. A second set of 4,500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used for fine tuning. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures and that CNNs can learn the distinctive features of these trajectories. Using transfer learning, a few strides could be sufficient to learn and identify new gaits.

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Section: Discussionmentioning

confidence: 99%

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Terrier

2020

Applied Sciences

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“…(2) Windowing: The signal is divided into smaller time slices called windows of fixed length and with a fixed overlap size with previous and next windows [13].…”

Section: Methodsmentioning

confidence: 99%

Automatic Fall Detection System using Sensing Floors

Daher

Diab

Najjar

et al. 2016

IJCIS

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Automatic fall detection is a major issue in taking care of the health of elderly people and has the potential of increasing autonomy and independence while minimizing the risks of living alone. It has been an active research area due to the large demand of the healthcare association for fall detection goods. Fortunately, due to the recent fast progression in sensing technologies, fall detection system becomes prospective. It permits to monitor elders and detect their falls, and consequently provides emergency support whenever needed. This paper describes the current work of detecting falls in independent living apartments using accelerometer concealed under tiles. We present the up-to-date advancement of data collection, feature extraction, feature selection, and signal changing detection, which are essential phases of this work.

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“…This drawback was exacerbated if there was more than one occupant in the house at any given time, since each person had to be tagged with RFID Tracking floors can detect where the person is [5,38]. Multiple objects can be recognized simultaneously by analysing the object's weight [58].…”

Section: Sensor Technology Devicesmentioning

confidence: 99%

A Review of Smart House Analysis Methods for Assisting Older People Living Alone

Sánchez

Pfeiffer

Skeie

2017

JSAN

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Smart Houses are a prominent field of research referring to environments adapted to assist people in their everyday life. Older people and people with disabilities would benefit the most from the use of Smart Houses because they provide the opportunity for them to stay in their home for as long as possible. In this review, the developments achieved in the field of Smart Houses for the last 16 years are described. The concept of Smart Houses, the most used analysis methods, and current challenges in Smart Houses are presented. A brief introduction of the analysis methods is given, and their implementation is also reported.

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Localization of Humans, Objects, and Robots Interacting on Load-Sensing Floors

Cited by 41 publications

References 48 publications

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Automatic Fall Detection System using Sensing Floors

A Review of Smart House Analysis Methods for Assisting Older People Living Alone

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