RAM: Radar-based activity monitor

Khan, Abdullah; Kukkapalli, Ruthvik; Waradpande, Piyush; Kulandaivel, Sekar; Banerjee, Nilanjan; Roy, Nirmalya; Robucci, Ryan

doi:10.1109/infocom.2016.7524361

Cited by 21 publications

(8 citation statements)

References 18 publications

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“…MAPE (Average absolute percentage error) is a statistical measurement parameter of how accurate a forecast system is. It measures this accuracy as a percentage, and it can be calculated as the average absolute percent error for each time period minus actual values divided by actual values which are given by (6) [30]:…”

Section: The Design Of the New Methods For Co2 Predictionmentioning

confidence: 99%

“…Chen et al (2018) proposed an activity recognition system guided by an unobtrusive sensor (ARGUS) with a facing direction detection accuracy, resulting from manually defined features, that reached 85.3%, 90.6%, and 85.2% [5]. Khan et al (2016) developed a low-cost heterogeneous Radar-Based Activity Monitoring (RAM) system for recognizing fine-grained activities in an SH with detecting accuracy of 92.84% [6]. Lee et al (2011) investigated the use of cameras and a distributed processing method for the automated control of lights in an SH, which provided occupancy reasoning and human activity analysis [7].…”

Section: Introductionmentioning

confidence: 99%

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Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Vaňuš¹,

Nedoma²,

Fajkus³

et al. 2020

Preprint

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This article introduces a new way of using a Fibre Bragg Grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a Smart Home (SH). CO2 sensors are used to determine the CO2 concentration of the monitored rooms in an SH. CO2 sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO2 prediction, by means of an Artificial Neural Network (ANN) with a Scaled Conjugate Gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO2 concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO2 prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO2 prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the Adaptive Filtration with of LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

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Section: The Design Of the New Methods For Co2 Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Vaňuš¹,

Nedoma²,

Fajkus³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Over the years, several studies have adopted hand-crafted feature based representations extracted from time-range, range-Doppler, cadence velocity maps [26,3], along with machine learning models such as support vector machines and random forests. In [2], the authors estimated the phase, velocity, rate of change, mean, standard deviation and range of the I and Q signals, and applied a random forest classifier for HAR classification. The authors of [7] proposed a dynamic range-Doppler trajectory (DRDT) method to recognize various human motion.…”

Section: Related Workmentioning

confidence: 99%

Multi-View CNN-LSTM Architecture for Radar-Based Human Activity Recognition

et al. 2022

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In this paper, we propose a multi-view Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) network which fuses multiple "views" of the time-range-Doppler radar data-cube for human activity recognition. It adopts the structure of convolutional neural networks to extract optimal frame based features from the time-range, time-Doppler and range-Doppler projections of the radar datacube. The CNN models are trained using an unsupervised Convolutional Auto-Encoder (CAE) topology. Afterwards, the pre-trained parameters of the encoder are fine-tuned to extract intermediate frame based representations, which are subsequently aggregated via LSTM networks for sequence classification. The temporal correlation among the views is explicitly learned by sharing the LSTM network weights across different views. Moreover, we propose range and Doppler energy dispersion and temporal difference based features as an input to the CNN-LSTM models. Furthermore, we investigate the use of target tracking features as an auxiliary side information. The proposed model is trained on datasets collected in both cluttered and uncluttered environments. For validation, an independent test dataset, with unseen participants, in a cluttered environment was collected. Fusion with auxiliary features improves the generalization by 5%, yielding an overall Macro F1-score of 74.7%.

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“…The basic principle of RF-based HAR systems is that the propagation paths of features pertaining to different activities. Consequently, we can exploit these unique features to distinguish different activities, hence significant progress on RF-based HAR has been achieved in the past few years [8]- [14], [16]- [19]. Among all wireless signals used for HAR, Wi-Fi is the most popular one owning to the ubiquitous deployment [8]- [15].…”

Section: Introductionmentioning

confidence: 99%

“…For HAR, another big issue is how to extract stable and unique features related to each activity. However, these features depend highly on the individuals: body size and personal habits can cause large variations [18], [19] in the features extracted. Fortunately, resorting to Convolutional Neural Network (CNN), the complex features of various types of signals such as images and video have been effectively extracted [20], [21].…”

Section: Introductionmentioning

confidence: 99%

RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Network

Chen,

Cai,

Zheng

et al. 2021

Preprint

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Human Activity Recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting Radio-Frequency (RF) signals arise as a promising alternative for HAR. Most of the latest device-free approaches require training a large deep neural network model in either time or frequency domain, entailing extensive storage to contain the model and intensive computations to infer human activities. Consequently, even with some major advances on device-free HAR, current device-free approaches are still far from practical in real-world scenarios where the computation and storage resources possessed by, for example, edge devices, are limited. To overcome these weaknesses, we introduce HAR-SAnet which is a novel RF-based HAR framework. It adopts an original signal adapted convolutional neural network architecture: instead of feeding the handcraft features of RF signals into a classifier, HAR-SAnet fuses them adaptively from both time and frequency domains to design an end-to-end neural network model. We apply point-wise grouped convolution and depth-wise separable convolutions to confine the model scale and to speed up the inference execution time. The experiment results show that the recognition accuracy of HAR-SAnet substantially outperforms the state-of-the-art algorithms and systems.

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RAM: Radar-based activity monitor

Cited by 21 publications

References 18 publications

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Multi-View CNN-LSTM Architecture for Radar-Based Human Activity Recognition

RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Network

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