Sequential Human Gait Classification With Distributed Radar Sensor Fusion

Li, Haobo; Mehul, Ajay; Kernec, Julien Le; Gürbüz, Sevgi Zübeyde; Fioranelli, Francesco

doi:10.1109/jsen.2020.3046991

Cited by 57 publications

(34 citation statements)

References 44 publications

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“…8. This data has been used to support research relating to crossfrequency training [26] and cross-modal fusion [27] in RF sensor networks, as well as distributed RF sensor fusion for sequential gait recognition [28].…”

Section: Indoor Human Activity Classification Datasetmentioning

confidence: 99%

Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms

Gusland

Christiansen

Torvik

et al. 2021

2021 IEEE Radar Conference (RadarConf21)

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In this paper, we discuss an "open radar initiative" aimed at promoting the sharing of radar datasets and a common framework for acquiring data. The framework is based on widely available and affordable short-range radar hardware (automotive FMCW radar transceivers). This framework and initiative are intended to create and promote access to a common shared dataset for the development and benchmarking of algorithms. While this is the norm for image processing and speech processing research, there has been reluctance in the radar community so far to create common datasets of shared data, often due to justified intellectual property or security classification reasons. Notable exceptions do exist, such as the MSTAR dataset of SAR images, which enabled great progress across the radar research community for a number of years. With this initiative, we hope to stimulate discussion and, with time, changes of practice in the radar research community. The main contribution of this work relative to previously shared datasets of radar data is that the proposed framework consists of a complete, integrated and replicable hardware and software pipeline, allowing users to not only download existing data, but also to acquire their own data with a compatible format that allows expansion and enrichment of the common dataset.

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Section: Indoor Human Activity Classification Datasetmentioning

confidence: 99%

Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms

Gusland

Christiansen

Torvik

et al. 2021

2021 IEEE Radar Conference (RadarConf21)

Self Cite

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“…The latter paper presents a method which is based on using one transmit–double receive technique. In [ 12 ], data processing is used to distinguish twelve individual gaits and five sequential gaits using frequency modulated continuous wave radar and three UWB radars simultaneously. In this case, UWB signal detection is based on analysing the identifiable Doppler shift patterns caused by the different gaits.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Wideband Radar-Based Indoor Activity Monitoring for Elderly Care

Hämäläinen

Mucchi

Caputo

et al. 2021

Sensors

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In this paper, we propose an unobtrusive method and architecture for monitoring a person’s presence and collecting his/her health-related parameters simultaneously in a home environment. The system is based on using a single ultra-wideband (UWB) impulse-radar as a sensing device. Using UWB radars, we aim to recognize a person and some preselected movements without camera-type monitoring. Via the experimental work, we have also demonstrated that, by using a UWB signal, it is possible to detect small chest movements remotely to recognize coughing, for example. In addition, based on statistical data analysis, a person’s posture in a room can be recognized in a steady situation. In addition, we implemented a machine learning technique (k-nearest neighbour) to automatically classify a static posture using UWB radar data. Skewness, kurtosis and received power are used in posture classification during the postprocessing. The classification accuracy achieved is more than 99%. In this paper, we also present reliability and fault tolerance analyses for three kinds of UWB radar network architectures to point out the weakest item in the installation. This information is highly important in the system’s implementation.

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“…Radar-based gait measurement methods were proposed to address the drawbacks of other sensor-based techniques [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Radar-based methods can remotely measure the velocity of whole human body parts without placing any constraints on the participant.…”

Section: Introductionmentioning

confidence: 99%

“…Because the MDR can measure micro-motions of humans, it is used for the recognition of detailed motions such as vital signs [ 9 ], gesture classification [ 10 ], and classifications of human gait types (e.g., classification with/without arm swinging [ 11 ] and slow/fast walking [ 12 ]). In addition, MDR-based techniques have been applied to obtain detailed gait measurement data that are used for personal identification [ 13 , 14 ] and the identification of gait type for rehabilitation and hospital applications [ 15 , 16 ]. However, the above-mentioned conventional studies have not focused on gait classification based on age-related gait changes, which are investigated in the field of biomechanics.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network

Hayashi

Saho

Shioiri

et al. 2021

Sensors

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To develop a daily monitoring system for early detection of fall risk of elderly people during walking, this study presents a highly accurate micro-Doppler radar (MDR)-based gait classification method for the young and elderly adults. Our method utilizes a time-series of velocity corresponding to leg motion during walking extracted from the MDR spectrogram (time-velocity distribution) in an experimental study involving 300 participants. The extracted time-series was inputted to a long short-term memory recurrent neural network to classify the gaits of young and elderly participant groups. We achieved a classification accuracy of 94.9%, which is significantly higher than that of a previously presented velocity-parameter-based classification method.

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Sequential Human Gait Classification With Distributed Radar Sensor Fusion

Cited by 57 publications

References 44 publications

Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms

Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms

Ultra-Wideband Radar-Based Indoor Activity Monitoring for Elderly Care

Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network

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