Ultrasonic sensor-based human detector using one-class classifiers

Sonia, .; Tripathi, Achyut Mani; Baruah, Rashmi Dutta; Nair, Shivashankar B.

doi:10.1109/eais.2015.7368797

Cited by 14 publications

(3 citation statements)

References 19 publications

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“…Kessler et al [6] report the use of vibration sensors to detect human beings. An ultrasonic sensor has been used by Sonia et al [15] to differentiate human from non-human things. However, no one claims 100% accuracy to detect the human presence in every possible scenario with a single sensor.…”

Section: Literature Surveymentioning

confidence: 99%

A Multimodal Human Sensing System for Assisted Living

Sonia¹,

Semwal²

2020

EAI Endorsed Trans Perv Health Tech

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Introduction: Advancements in sensor technology have resulted in smart systems that can analyze, interpret and understand their surroundings for decision making. Using such intelligent systems for environments such as smart homes, device automation, and hospitals, makes sensing human presence an essential requirement. Human sensing becomes critical, especially in assisted living scenarios such as in elderly care. However, mechanisms for human sensing are not foolproof because of the dynamic nature and ability of human beings to deliberately mislead the sensors. Objective: Objective of paper is to detect human presence in varying environment using non-intrusive sensors. Method: Besides, sensors have inherent limitations, due to either their mechanism of sensing or the environmental conditions, which can cause them to fail in human detection. These limitations can, at times, cause sensors to provide useless data to the system. In this paper, we propose an adaptive multi-modal human sensing mechanism which can autonomously identify and ignore unnecessary data from a set of sensors, thereby reducing computation complexity, reducing false alarm rate and yielding better performance. The effect of sensing when the human being is in motion has also been studied. Results: The results portrayed in the paper prove the efficacy of the proposed multi-modal system over its single sensor counterparts when used in changing environments and other proposed multi-modal human sensing. Conclusions: Human sensing is vital to many smart applications such as smart homes, traffic management systems, human-computer interfaces, etc. Since human beings, in general, are always on the move, the use of a dedicated sensor could fail to detect human presence, especially when the ambient parameters around the sensor change. In this paper, a multi-modal human sensing approach has thus been prescribed for overcoming this issue. This work described focused on automating the identification of inappropriate data relayed by some sensors under certain environmental conditions. Experiments reported include both cases-when the sensors are mounted on a static unit (door frame) and also on a mobile robot. The corresponding results reveal that a combination of sensors outperforms the use of individual dedicated sensors for human detection. Analyses of the walking speed of human being have also been studied which endorse the robustness of the approach. However, different directions of motion need exploration in future.

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Section: Literature Surveymentioning

confidence: 99%

A Multimodal Human Sensing System for Assisted Living

Sonia¹,

Semwal²

2020

EAI Endorsed Trans Perv Health Tech

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“…Camera-based solutions can detect human targets [14], [15], but higher computational cost, sensitivity to ambient light, and privacy concerns limit their deployment. Passive infrared (PIR) sensors and ultrasonic sensors were used for detecting human subjects in [16]- [18]. However, PIR sensors are insensitive to low levels of motion, and ultrasonic sensors require a direct line of sight between the sensor and the occupants.…”

Section: Introductionmentioning

confidence: 99%

Human Presence Sensing and Gesture Recognition for Smart Home Applications With Moving and Stationary Clutter Suppression Using a 60-GHz Digital Beamforming FMCW Radar

et al. 2021

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With the advancement of radio frequency (RF) assisted smart home technology, it is critical for the RF sensors deployed indoors to isolate the target of interest from unwanted clutter sources. This paper presents a novel method for suppressing both moving and stationary clutters in an indoor environment to localize stationary human subjects with a millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The method derives its roots from the intrinsic high-pass filter (HPF) characteristic of the exponential moving average (EMA) algorithm, a preferred approach for background stationary clutter suppression. In this work, emphasis was laid on expanding the capability to detect and suppress unwanted moving clutter sources in the indoor environment along with stationary clutters, which has not been widely explored before. The proposed method removes motion artifacts so that the characteristic respiratory signal can be identified for human-aware localization. The paper provides experimental validation of the proposed method, wherein a 60-GHz FMCW radar with digital beamforming (DBF) capability was used to identify the 2-D location of a sitting human subject, with a moving window curtain in the background acting as a strong moving clutter source along with other stationary clutters. In addition, a lateral hand gesture recognition technique is presented, wherein the EMA algorithm was used to enhance the signature of the hand motion. The instantaneous position of the hand at the beginning and end of the gesture was determined to classify the gesture as a left-to-right or right-to-left hand swipe.INDEX TERMS Exponential moving average (EMA) algorithm, frequency-modulated continuous-wave (FMCW) radar, gesture recognition, human localization, moving clutter suppression, smart homes.

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“…Many solutions have been developed to solve the problem of human detection. The existing human occupancy sensing modalities include a visual camera [ 4 ], as well as lidar [ 5 ], radar [ 6 , 7 ], infrared [ 8 ], and ultrasonic sensors [ 9 ]. These modalities all have their own individual strengths and weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Human Occupancy Detection via Passive Cognitive Radio

Liu

Vakil

et al. 2020

Sensors

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Human occupancy detection (HOD) in an enclosed space, such as indoors or inside of a vehicle, via passive cognitive radio (CR) is a new and challenging research area. Part of the difficulty arises from the fact that a human subject cannot easily be detected due to spectrum variation. In this paper, we present an advanced HOD system that dynamically reconfigures a CR to collect passive radio frequency (RF) signals at different places of interest. Principal component analysis (PCA) and recursive feature elimination with logistic regression (RFE-LR) algorithms are applied to find the frequency bands sensitive to human occupancy when the baseline spectrum changes with locations. With the dynamically collected passive RF signals, four machine learning (ML) classifiers are applied to detect human occupancy, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), and linear SVM with stochastic gradient descent (SGD) training. The experimental results show that the proposed system can accurately detect human subjects—not only in residential rooms—but also in commercial vehicles, demonstrating that passive CR is a viable technique for HOD. More specifically, the RFE-LR with SGD achieves the best results with a limited number of frequency bands. The proposed adaptive spectrum sensing method has not only enabled robust detection performance in various environments, but also improved the efficiency of the CR system in terms of speed and power consumption.

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Ultrasonic sensor-based human detector using one-class classifiers

Cited by 14 publications

References 19 publications

A Multimodal Human Sensing System for Assisted Living

A Multimodal Human Sensing System for Assisted Living

Human Presence Sensing and Gesture Recognition for Smart Home Applications With Moving and Stationary Clutter Suppression Using a 60-GHz Digital Beamforming FMCW Radar

Human Occupancy Detection via Passive Cognitive Radio

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