Automatic human fall detection in fractional fourier domain for assisted living

Liu, Shengheng; Zeng, Zhengxin; Zhang, Yimin D.; Fan, Tingting; Shan, Tao; Tao, Ran

doi:10.1109/icassp.2016.7471785

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…Considering that human movement also contains certain frequency information, the accuracy of classification can be improved to some extent by introducing frequency-related features. erefore, by introducing fractional Fourier transform, this paper extracts features from the perspective of the fraction domain and makes it contain both time domain and frequency information [18].…”

Section: Domain Transformationmentioning

confidence: 99%

Preimpact Fall Detection for Elderly Based on Fractional Domain

Liu

Zhang

et al. 2021

Mathematical Problems in Engineering

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The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features.

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Section: Domain Transformationmentioning

confidence: 99%

Preimpact Fall Detection for Elderly Based on Fractional Domain

Liu

Zhang

et al. 2021

Mathematical Problems in Engineering

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“…B. Jokanovic et al in [8][9] used a vector network analyzer (VNA) working as a continuous wave (CW) radar, with carrier frequency at 6GHz, to collect Doppler patterns of test subjects, and then apply a neural network to classify the motions. Shengheng Liu et al in [10] applied short-time fractional Fourier transform (STFrFT) on the data collected by a C-band frequency-modulated continuous-wave (FMCW) radar, and then detected fall [12]. However, until 2017, just one year prior to when our research was conducted [13], none of them used a higher frequency radar operating in W band, for example in 77GHz or 90GHz, as highlighted in [14].…”

Section: Introductionmentioning

confidence: 99%

Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs

Jin

Zhang

Sengupta

et al. 2019

2019 IEEE Radar Conference (RadarConf)

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To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to track multiple patients and detect the scattering point cloud of each one. For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature. A three-layer CNN model is created to classify the behavior for each patient. The tracking and point clouds detection algorithm was also implemented on an mmWave radar hardware platform with an embedded graphics processing unit (GPU) board to collect Doppler pattern and run the CNN model. A training dataset of six types of behavior were collected, over a long duration, to train the model using Adam optimizer with an objective to minimize cross-entropy loss function. Lastly, the system was tested for real-time operation and obtained a very good inference accuracy when predicting each patient's behavior in a two-patient scenario.

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“…The use of Fractional Fourier Transform (FrFT) and Fractional Short Time Fourier Transform (FrSTFT) was proposed in [49] to achieve higher signal energy concentration and improve classification results for scenarios with low signalto-noise ratio. The processing scheme assumed to use the FrFT on the data to compare the results with a threshold, and then initiate the classification routine based on the FrSTFT if this threshold is exceeded.…”

Section: A Fall Detection Using Micro-dopplermentioning

confidence: 99%

“…Type of radar sensor [18], [19], [47] Commercial pulse-Doppler radar operating at 5.8 GHz [49] Commercial Frequency Modulated Continuous Wave (FMCW) radar operating at 5.8 GHz (100 MHz bandwidth) [47], [52]- [55] Vector Network Analyser operating as a Doppler radar at 8 GHz (1 kHz sampling rate) [4] Vector Network Analyser operating as a Doppler radar at 6 GHz (1 kHz sampling rate) [50], [51] CW Doppler radar operating at 24 GHz (1024 Hz sampling rate) or receiver array operating at 2.457 GHz Alternative is a combination of these two sensors, with the array operating at 800 MHz [56] Commercial CW Doppler radar operating at 24 GHz (1 kHz sampling rate) The availability in the market of inexpensive RGB-D sensors fostered researchers working in the computer vision area to combine depth data and RGB images. The depth information, previously available using expensive Time-of-Flight (TOF) cameras or multiple calibrated cameras, has brought many advantages in the development of vision-based solutions.…”

Section: Referencesmentioning

confidence: 99%

“…MFCC coefficients [53]- [56] Empirical features extracted from spectrograms after STFT [49] Features extracted from fractional FFT and STFT [18], [48], [56] Features from Wavelet Transform [57] Empirical features after EMBD [4][59] Feature extraction bypassed by using CNNs [65] Additional features extracted from range information [66] Velocity information from phase of radar range profiles [67]- [69] Velocity information from I-Q radar data [50], [52] Features from subspace decomposition of signals received at array …”

Section: Referencesmentioning

confidence: 99%

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Radar and RGB-Depth Sensors for Fall Detection: A Review

et al. 2017

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Abstract-This paper reviews recent works in the literature on the use of systems based on Radar and RGB-Depth sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users' acceptance and compliance, compared with other sensor technologies such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.

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Automatic human fall detection in fractional fourier domain for assisted living

Cited by 16 publications

References 16 publications

Preimpact Fall Detection for Elderly Based on Fractional Domain

Preimpact Fall Detection for Elderly Based on Fractional Domain

Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs

Radar and RGB-Depth Sensors for Fall Detection: A Review

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