An Algorithm for Estimating Gait Parameters Through a Commercial Sensorized Carpet

Bagarotti, Riccardo; Zini, Elisa Maria; Salvi, Elisa; Sacchi, Lucia; Quaglini, Silvana; Lanzola, Giordano

doi:10.1109/rtsi.2018.8548404

Cited by 1 publication

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first type of these floor sensors measures the impedance of electric field couplings for an array of sensor plates, which is also called near field imaging [33]. A second type measures the electric capacitance, like the ELSI ® Smart Floor by Mari Mils [34,35], or SensFloor ® by Future-Shape [3,[36][37][38][39][40][41][42][43]. The latter one was used for the data acquisition here, and is described in more detail later-on.…”

Section: Sensors For Gait and Behaviour Analysismentioning

confidence: 99%

“…Most commonly, the system is used in elderly care facilities for fall detection [40], but also for ambient assisted living at home [42]. Various approaches exist to extract gait parameters from the sensor data, for instance with an automatic step detection algorithm [36]. The SensFloor base material is a three-layered composite, with a thin aluminium foil at the bottom, a polyester fleece in the middle with a height of 3 mm, and a thin top layer of polyester fleece which is metal-coated and thereby electrically conductive.…”

Section: Capacitive Floor Sensormentioning

confidence: 99%

See 1 more Smart Citation

Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks

Hoffmann

Brodowski

Steinhage³

et al. 2021

Sensors

View full text Add to dashboard Cite

Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person’s state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care.

show abstract