Smart carpet using differential piezoresistive pressure sensors for elderly fall detection

Chaccour, Kabalan; Darazi, Rony; Hassans, Amir Hajjam el; Andrès, Emmanuel

doi:10.1109/wimob.2015.7347965

Cited by 42 publications

(25 citation statements)

References 6 publications

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“…Vision-based gait analysis use either a video camera [14], [15], a thermal vision sensor [16], [17] or a depth camera [18], [19]. Alternatively, the environment-based gait assessment rely on floor-deployed pressure sensors [20], [21] and infrared sensors [22], [23]. However, both such solutions require a controlled research facility for the analysis that limits their applicability in external/outdoor environments [24].…”

Section: Introductionmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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Section: Introductionmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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“…There also exist studies using the piezo-electric effect in order to obtain the location and activity information of the users. The study of 'smart carpets' done by Chaccour et al [19] does not cite indoor localisation as its main objective. However, this implementation could be used for very coarse location estimation as well.…”

Section: Other Modalitiesmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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“…Tanaka et al [13] designed a smart carpet from an array of pressure-sensing tiles that can be easily assembled on a floor to monitor user position, motion and falls. Chaccour et al [14] embedded a matrix of differential piezoresistive pressure sensors into a carpet for fall detection. Ho et al [15] proposed a textile technology for making smart carpets from tension-sensitive electro-conductive yarns.…”

Section: Related Researchmentioning

confidence: 99%

New Technique for Posture Identification in Smart Prayer Mat

Kasman

Moshnyaga

2017

Electronics

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Smart praying mats are essential to help old and forgetful Muslims perform their religious needs. Due to the binary representation of pressure sensors embedded into the mat for posture identification, existing smart praying systems either use large sensing arrays, thus becoming expensive and bulky, or utilize only a limited number of sensors, minimizing the cost of posture recognition accuracy. This article presents a new technique for detecting human postures and counting posture cycles by a smart mat. Unlike related solutions, the proposed technique identifies postures by voltage levels observed from five sensors only. The technique has been implemented in a prototype smart mat and experimentally evaluated by 30 Islam worshipers. The results show that it provides unobtrusive and robust (100%) recognition of all six postures of the Muslim praying cycle and reliable cycle counting. The implementation is inexpensive, easy to use and quite helpful for users.

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Smart carpet using differential piezoresistive pressure sensors for elderly fall detection

Cited by 42 publications

References 6 publications

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Sensor Modalities and Fusion for Robust Indoor Localisation

New Technique for Posture Identification in Smart Prayer Mat

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