Real-time Daily Activity Classification with Wireless Sensor Networks using Hidden Markov Model

He, Jin; Li, Huaming; Tan, Jindong

doi:10.1109/iembs.2007.4353008

Cited by 62 publications

(39 citation statements)

References 5 publications

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“…To date, however, no study has considered the on-board classification of tri-axial accelerometer data for real-time automatic behaviour classification. Related studies have considered the real-time monitoring of human activities based on accelerometer data [24][25][26][27][28][29][30]. These studies demonstrate good performance but are also less constrained in terms of battery life and communication bandwidth since users are co-operative and can be relied on to recharge the batteries and to be within reception of standard communication technologies, such as Wi-Fi and cellular networks, which can be used to transfer data.…”

Section: Introductionmentioning

confidence: 99%

Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis)

et al. 2017

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Background:The ability to study animal behaviour is important in many fields of science, including biology, behavioural ecology and conservation. Behavioural information is usually obtained by attaching an electronic tag to the animal and later retrieving it to download the measured data. We present an animal-borne behaviour classification system, which captures and automatically classifies three-dimensional accelerometer data in real time. All computations occur on specially designed biotelemetry tags while attached to the animal. This allows the probable behaviour to be transmitted continuously, thereby providing an enhanced level of detail and immediacy. Results:The performance of the animal-borne automatic behaviour classification system is presented for sheep and rhinoceros. For sheep, a classification accuracy of 82.40% is achieved among five behavioural classes (standing, walking, grazing, running and lying down). For rhinoceros, an accuracy of 96.10% is achieved among three behavioural classes (standing, walking and lying down). The estimated behaviour was established approximately every 5.3 s for sheep and 6.5 s for rhinoceros. Conclusions:We demonstrate that accurate on-animal real-time behaviour classification is possible by successful design, implementation and deployed on sheep and rhinoceros. Since the bandwidth required to transmit the behaviour class is lower than that which would be required to transmit the accelerometer measurements themselves, this system is better suited to low-power and error-prone data communication channels that may be expected in the animals habitat.

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Section: Introductionmentioning

confidence: 99%

Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis)

et al. 2017

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“…porting resource-intensive HMMs to a mobile device. As a generative model which does not involve many mathematical calculations, discrete HMM has widely been used for smoothing the classification results by finding the most probable output, considering one or number of previous states [Wu et al, 2007], [He et al, 2007]. For a detailed discussion of related issues the reader is referred to [Attalah and Yang, 2009].…”

Section: Generative Modelsmentioning

confidence: 99%

A survey on smartphone-based systems for opportunistic user context recognition

2013

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The ever growing computation and storage capability of mobile phones has given rise to mobile centric context recognition systems, which are able to sense and analyze the context of the carrier so as to provide an appropriate level of service. Particularly, as nonintrusive autonomous sensing and context recognition are one of the most desirable characteristics of a personal sensing system; commendable efforts have been made to develop opportunistic sensing techniques on mobile phones. The resulting combination of these approaches has ushered in a new realm of applications, namely opportunistic user context recognition with mobile phones. This article surveys the existing research and approaches towards realization of such systems. In doing so, the typical architecture of a mobile centric user context recognition system as a sequential process of sensing, pre-processing and context recognition phases are introduced. The main techniques used for the realization of the respective processes during these phases are described, highlighting strengths and limitations of those. In addition, lessons learned from previous approaches are presented as motivation for future research. Finally, several open challenges are discussed as possible ways to extend the capabilities of current systems and improve their real-world experience.

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“…Wearable sensors can measure limb movements, posture, and muscular activity, and can be applied to a range of clinical settings including gait analysis [60], [64], [73], activity classification [29], [52], athletic performance [3], [51], and neuromotor disease rehabilitation [49], [57]. In a typical scenario, a patient wears up to eight sensors (one on each limb segment) equipped with MEMS accelerometers and gyroscopes.…”

Section: B Motion and Activity Monitoringmentioning

confidence: 99%

Wireless Sensor Networks for Healthcare

et al. 2010

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In healthcare, there is a strong need to collect physiological data and sensor networking; this paper reviews recent studies and points to the need for future research.By JeongGil Ko, Chenyang Lu, Mani B. Srivastava, John A. Stankovic, Fellow IEEE, Andreas Terzis, and Matt Welsh ABSTRACT | Driven by the confluence between the need to collect data about people's physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from personal to urban and the recent availability of the technologies that enable this data collection, wireless sensor networks for healthcare have emerged in the recent years. In this review, we present some representative applications in the healthcare domain and describe the challenges they introduce to wireless sensor networks due to the required level of trustworthiness and the need to ensure the privacy and security of medical data. These challenges are exacerbated by the resource scarcity that is inherent with wireless sensor network platforms. We outline prototype systems spanning application domains from physiological and activity monitoring to large-scale physiological and behavioral studies and emphasize ongoing research challenges.

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Real-time Daily Activity Classification with Wireless Sensor Networks using Hidden Markov Model

Cited by 62 publications

References 5 publications

Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis)

Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis)

A survey on smartphone-based systems for opportunistic user context recognition

Wireless Sensor Networks for Healthcare

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