Radar classifications of consecutive and contiguous human gross‐motor activities

Amin, Moeness G.; Guendel, Ronny G.

doi:10.1049/iet-rsn.2019.0585

Cited by 12 publications

(8 citation statements)

References 35 publications

(36 reference statements)

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“…As an example of the probability outputs provided by the Softmax classifier, we show in Table I four random samples from validating the Softmax classifier. We picked four samples from the classes (1) walking, (4) standing up from sitting, (7) falling from walking, and (9) falling from standing. It can be seen that walking has a high prediction accuracy (97.51%) with almost no confusion versus another class.…”

Section: Results For Different Test Methodologiesmentioning

confidence: 99%

“…In this respect, researchers have demonstrated detecting sequences of ADLs by using deep learning techniques, such as the long short-term memory (LSTM) [6]. Also, state separation between translational and in-place activities was introduced for the usage of dynamic classifiers increasing the performance with backward in time classification and "re-visiting of activities" [7]- [9]. Other promising results showed a categorization of different walking gaits in unconstrained directions associated to different subjects [10].…”

Section: Introductionmentioning

confidence: 99%

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Continuous human activity recognition for arbitrary directions with distributed radars

Guendel

Unterhorst

Gambi

et al. 2021

2021 IEEE Radar Conference (RadarConf21)

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Continuous Activities of Daily Living (ADL) recognition in an arbitrary movement direction using five distributed pulsed Ultra-Wideband (UWB) radars in a coordinated network is proposed. Classification approaches in unconstrained activity trajectories that render a more natural occurrence for Human Activity Recognition (HAR) are investigated. Feature and decision fusion methods are applied to the priorly extracted handcrafted features from the range-Doppler. A following multinomial logistic regression classifier, commonly known as Softmax, provides explicit probabilities associated with each target label. The outputs of these classifiers from different radar nodes were combined with a probability prediction balancing approach over time to improve performances. The final results show average improvements between 6.8% and 17.5% compared to the usage of any single radar in unconstrained directions.

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Section: Results For Different Test Methodologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continuous human activity recognition for arbitrary directions with distributed radars

Guendel

Unterhorst

Gambi

et al. 2021

2021 IEEE Radar Conference (RadarConf21)

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“…Feature extraction from radar data can leverage the increasing dimensionality of radar signals (e.g., higher range resolution), frontends with multiple input and multiple output capabilities, increasing angular and spatial diversity, and on the various data representations ranging from raw IQ data directly sampled by the radar directly to range-time, range-Doppler, range-azimuth, angle-of-arrival, spectrograms, cadence velocity diagrams, cepstrograms, phase plots [46], [47], cyclostationarity signatures [48], radon transform signatures [49], and other composite views e.g., range-Doppler surfaces [30]. Thus, open research questions remain as to what format or combination of formats are most suitable for the classification process, perhaps exploiting forms of cognition that modify accordingly such processes depending on the specific activities to be classified.…”

Section: Future Workmentioning

confidence: 99%

The Human Activity Radar Challenge: Benchmarking Based on the ‘Radar Signatures of Human Activities’ Dataset From Glasgow University

Yang

Kernec

Romain

et al. 2023

IEEE J. Biomed. Health Inform.

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Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting conditions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.

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“…The DTL is used to separate translation activities from inplace activities, whereas within in-place activities with almost no range swath the DTL cannot be applied for discriminating between multiple consecutive in-place activities. For separating such in-place activities, we rely on an energy detector, known as the Power Burst Curve (PBC) [13], [17].…”

Section: Power Burst Curve (Pbc)mentioning

confidence: 99%

Derivative Target Line (DTL) for Continuous Human Activity Detection and Recognition

Guendel

Fioranelli

Yarovoy

2020

2020 IEEE Radar Conference (RadarConf20)

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In this paper, we investigate the classification of Activities of Daily Living (ADL) by using a pulsed ultra-wideband radar. Specifically, we focus on contiguous activities that can be inseparable in time and share a common transition, such as walking and falling. The range-time data domain is deliberately exploited to determine transitions from translation activities to in-place activities and vice versa, using a simple, yet effective approach based on the proposed Derivative Target Line (DTL). The separation of different in-place activities is then addressed using an energy detector finding the onset and offset times. Furthermore, the possible ADL for classification are limited at any decision stage based on kinematic constraints of human movements. We show that such limitation of classes at any given time leads to a classification improvement over a classifier containing always all ADL classes.

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Radar classifications of consecutive and contiguous human gross‐motor activities

Cited by 12 publications

References 35 publications

Continuous human activity recognition for arbitrary directions with distributed radars

Continuous human activity recognition for arbitrary directions with distributed radars

The Human Activity Radar Challenge: Benchmarking Based on the ‘Radar Signatures of Human Activities’ Dataset From Glasgow University

Derivative Target Line (DTL) for Continuous Human Activity Detection and Recognition

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