Smart bedroom for elderly using kinect

Booranrom, Yoottana; Watanapa, Bunthit; Mongkolnam, Pornchai

doi:10.1109/icsec.2014.6978235

Cited by 17 publications

(10 citation statements)

References 9 publications

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“…Time-of-flight (ToF) cameras that provide 3D measurements of a scene have been used for tracking occupants [7] and recognizing poses [6] and gestures in smart rooms. Booranrom et al [3] developed a smart bedroom using Microsoft Kinect sensors to assist the elderly to turn on and off remote devices without touching them or using remote controls. The system also monitored signs of abnormality in the behavior of the elderly occupant.…”

Section: Related Workmentioning

confidence: 99%

Arrays of single pixel time-of-flight sensors for privacy preserving tracking and coarse pose estimation

Bhattacharya

Radke

2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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We present a method for real-time person tracking and coarse pose estimation in a smart room using a sparse array of single pixel time-of flight (ToF) sensors mounted in the ceiling of the room. The single pixel sensors are relatively inexpensive compared to commercial ToF cameras and are privacy preserving in that they only return the range to a small set of hit points. The tracking algorithm includes higher level logic about how people move and interact in a room and makes estimates about the locations of people even in the absence of direct measurements. A maximum likelihood classifier based on features extracted from the time series of ToF measurements is used for robust pose classification into sitting, standing and walking states. We use both computer simulation and real-world experiments to show that the algorithms are capable of robust person tracking and pose estimation even with a sensor spacing of 60 cm (i.e., 1 sensor per ceiling tile).

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Section: Related Workmentioning

confidence: 99%

Arrays of single pixel time-of-flight sensors for privacy preserving tracking and coarse pose estimation

Bhattacharya

Radke

2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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“…On the other hand, considering the constraints raised by the privacy of the students inhabiting the rooms, video recording and video surveillance systems did not represent a viable option. We therefore chose a motion sensing system based on skeletal tracking (Booranrom et al, 2014;Du et al, 2015;Gasparrini et al, 2015;Visutarrom et al, 2015;Liu et al, 2018). The current implementation is based on Microsoft's Kinect One motion sensing system, yet it is easily portable to any skeletal tracking device that can provide the 3D coordinates of the chosen set of skeletal joints.…”

Section: Experimental Set-upmentioning

confidence: 99%

“…A similar weakness affects the SVM algorithm, which nevertheless is considered relatively memory efficient. Achieving the best classification results, for any given problem, requires setting several key parameters that need to be chosen correctly (Bishop, 2006). Artificial neural networks, such as multi-layer perceptron (MLP) algorithm, can be applied to many smart home problems, ranging from activity classification, to novelty and anomaly recognition, to activity prediction (Begg and Hassan, 2006;Hussein et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Automatic Pose Recognition for Monitoring Dangerous Situations in Ambient-Assisted Living

Guerra

Ramat

Beltrami

et al. 2020

Front. Bioeng. Biotechnol.

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Continuous monitoring of frail individuals for detecting dangerous situations during their daily living at home can be a powerful tool toward their inclusion in the society by allowing living independently while safely. To this goal we developed a pose recognition system tailored to disabled students living in college dorms and based on skeleton tracking through four Kinect One devices independently recording the inhabitant with different viewpoints, while preserving the individual's privacy. The system is intended to classify each data frame and provide the classification result to a further decisionmaking algorithm, which may trigger an alarm based on the classified pose and the location of the subject with respect to the furniture in the room. An extensive dataset was recorded on 12 individuals moving in a mockup room and undertaking four poses to be recognized: standing, sitting, lying down, and "dangerous sitting." The latter consists of the subject slumped in a chair with his/her head lying forward or backward as if unconscious. Each skeleton frame was labeled and represented using 10 discriminative features: three skeletal joint vertical coordinates and seven relative and absolute angles describing articular joint positions and body segment orientation. In order to classify the pose of the subject in each skeleton frame we built a two hidden layers multi-layer perceptron neural network with a "SoftMax" output layer, which we trained on the data from 10 of the 12 subjects (495,728 frames), with the data from the two remaining subjects representing the test set (106,802 frames). The system achieved very promising results, with an average accuracy of 83.9% (ranging 82.7 and 94.3% in each of the four classes). Our work proves the usefulness of human pose recognition based on machine learning in the field of safety monitoring in assisted living conditions.

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“…Smart bedroom for elderly using Kinect is also purposed and implemented to help them by providing smart home environments using Kinect v1 [13]. This system mainly detects the arms motion and gives a warning for the risk of bed falling.…”

Section: Related Workmentioning

confidence: 99%

Sleep Monitoring System Using Kinect Sensor

Lee

Hong

Sung-Yong

2015

International Journal of Distributed Sensor Networks

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Sleep activity is one of crucial factors for determining the quality of human life. However, a traditional sleep monitoring system onerously requires many devices to be attached to human body for achieving sleep related information. In this paper, we proposed and implemented the sleep monitoring system which can detect the sleep movement and posture during sleep using a Microsoft Kinect v2 sensor without any body attached devices. The proposed sleep monitoring system can readily gather the sleep related information that can reveal the sleep patterns of individuals. We expect that the analyzed sleep related data can significantly improve the sleep quality.

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Smart bedroom for elderly using kinect

Cited by 17 publications

References 9 publications

Arrays of single pixel time-of-flight sensors for privacy preserving tracking and coarse pose estimation

Arrays of single pixel time-of-flight sensors for privacy preserving tracking and coarse pose estimation

Automatic Pose Recognition for Monitoring Dangerous Situations in Ambient-Assisted Living

Sleep Monitoring System Using Kinect Sensor

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