Elderly care notification system using hand posture recognition

Sawasdee, Sutang; Pumrin, Suree

doi:10.1109/dictap.2014.6821701

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…We found that 4 of the 7 compared works use video/image capture [27,29,30,33] for gesture recognition. Although these systems have high percentages of accuracy in gesture recognition, they also raise several issues and constraints.…”

Section: Discussionmentioning

confidence: 99%

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Internet of Things Assisted Monitoring Using Ultrasound-Based Gesture Recognition Contactless System

et al. 2021

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In the last decade, we have seen an increase in the use cases of digital technologies in our daily lives, including advanced systems such as our mobile applications, smart digital kiosks for intelligent retailing, touch screens for ordering food, or simple implementations such as the ticket machines in the butchers. However, from the perspective of interaction design, those sensing systems suffer from a range of limitations like low usability and poor hygiene. For example, the elderly or the disabled normally have huge difficulties when interacting with these types of digital systems. Moreover, the recent SARS-CoV-2 pandemic has made us to rethink the contact manner for interaction with the digital devices. Hence, in this paper, we have presented a novel solution for digital interaction through a contactless manner. Such a system can provide human gesture recognition and therefore it can be integrated into others to achieve contactless control. We have implemented a prototype based on cost efficient sensors to validate the idea's feasibility. A sequence of real world experiments has also been conducted to evaluate its performance. This system is composed with 1) piece of hardware-a grid of ultrasound sensors to capture the distance information from a human body; 2) a piece of software to analyse the data for gesture predictions. The analysis results then will be transformed into control commands to interact with the attached system. The experimental results have shown that the proposed system is capable of providing a contactless Human Computer Interaction (HCI), and also has a great opportunity to replace existing touching interaction manner with a remote control scheme.

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Section: Discussionmentioning

confidence: 99%

“…These solutions typically use sensors, computers, mobile devices, telehealth systems and rely on wireless and cloud computing technologies [26]. In this sense, several gesture-based contactless solutions were proposed for ill, disable or senior people [27,28,29]. Most of the solutions use cameras for gesture recognition.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Assisted Monitoring Using Ultrasound-Based Gesture Recognition Contactless System

et al. 2021

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“…An assistive hand posture recognition system for elderly care is proposed in [9]. The system uses Haar-like feature, Principal Component Analysis (PCA), and simulation web server for detection, recognition, and alert by order.…”

Section: Introductionmentioning

confidence: 99%

Hand gesture recognition in confined spaces with partial observability and occultation constraints

Shirkhodaie

Chan

2016

SPIE Proceedings

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Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.

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“…In [20], a prototype system of fall detection application designed for smart phone is proposed. Sawasdee et al [21] propose a hand posture recognition as an assistive tool for elderly care. In [22], a drowsy driver detection system has been developed, using video processing analyzing eyes blinking concepts for measuring eyes closure duration and head posture estimation to verify the driver vigilance state.…”

Section: Related Workmentioning

confidence: 99%

Reverse recognition of body postures using on‐body radio channel characteristics

Yang

Fang

Ren

et al. 2017

IET Microwaves, Antennas & Propagation

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the problem of posture detection is of considerable significance for assisted living (AL). In most cases, radio channel models for WBANs are fixed when a specific body posture is considered. To the best of our knowledge, little work has been done on the reverse body posture information extraction using WBAN radio channel characteristics. This paper aims to classify human postures from on-body narrowband wireless channel information. It is demonstrated that by applying the random forest (RF) classification technique, the action of the human body can be detected. The classification error is perfectly acceptable for RF algorithm. Two propagation environments were compared and the results indicate that the classification error is less in the anechoic chamber (21.39%). In summary, this paper provides a novel approach to detect human body postures by using body-centric wireless channel information, and will be beneficial for AL.

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Elderly care notification system using hand posture recognition

Cited by 7 publications

References 16 publications

Internet of Things Assisted Monitoring Using Ultrasound-Based Gesture Recognition Contactless System

Internet of Things Assisted Monitoring Using Ultrasound-Based Gesture Recognition Contactless System

Hand gesture recognition in confined spaces with partial observability and occultation constraints

Reverse recognition of body postures using on‐body radio channel characteristics

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