A system for ubiquitous fall monitoring at home via a wireless sensor network and a wearable mote

Paoli, Roberto; Fernández-Luque, Francisco J.; Domenech, G.; Martínez, Félix; Zapata, Juan; Ruíz, Ramón Eduardo

doi:10.1016/j.eswa.2011.11.061

Cited by 57 publications

(19 citation statements)

References 13 publications

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“…In Brezmes, Gorricho, and Cotrina (2009), it can be observed that the accelerometer sensor is placed in a glove and a multitude of activities are recognized depending on the movement of the hand. In contrast, other studies use either various sensors placed on many parts of the body (Bicocchi, Mamei, & Zambonelli, 2010;Lepri, Mana, Cappelletti, Pianesi, & Zancanaro, 2010) or a wearable wireless sensor node with a static wireless non-intrusive sensory infrastructure (Paoli, Fernández-Luque, & Zapata, 2011) to recognize these activities. According to certain comparative studies and previous research based on multiple sensors, these last two types of sensors provide greater accuracy.…”

Section: Introductionmentioning

confidence: 99%

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Concepción

Morillo

Abril

et al. 2014

Expert Systems with Applications

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Human activity recognition systems are currently implemented by hundreds of applications and, in recent years, several technology manufacturers have introduced new wearable devices for this purpose. Battery consumption constitutes a critical point in these systems since most are provided with a rechargeable battery. In this paper, by using discrete techniques based on the Ameva algorithm, an innovative approach for human activity recognition systems on mobile devices is presented. Furthermore, unlike other systems in current use, this proposal enables recognition of high granularity activities by using accelerometer sensors. Hence, the accuracy of activity recognition systems can be increased without sacrificing efficiency. A comparative is carried out between the proposed approach and an approach based on the well-known neural networks.

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Section: Introductionmentioning

confidence: 99%

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Concepción

Morillo

Abril

et al. 2014

Expert Systems with Applications

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“…1 T , 2 T , 3 T are the boundary constraints, which meet the QoS constraints in cloud computing environment.…”

Section: Ant Colony Algorithm With Three Constraint Conditionsmentioning

confidence: 99%

“…For the cloud of job scheduling, Guo proposed the Meta-scheduling model and the Meta-scheduling policy of a cloud environment. The model and job scheduling strategy can ensure that the entire data center's power consumption or carbon dioxide emissions minimum [1]. Alrokayan uses the cloud to expand the cluster to deal with the local cluster resource scarcity, and use some job scheduling strategy to evaluate the performance of the job, analyze the resource cost.…”

Section: Introductionmentioning

confidence: 99%

Application of Ant Colony Algorithm in Cloud Resource Scheduling Based on Three Constraint Conditions

Yang¹,

Aiwan²

2016

Advanced Science and Technology Letters

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Abstract. Aiming at the problem of resource scheduling in cloud computing, an ant colony algorithm based on the three constraint conditions is proposed. The implementation of this method is divided into three steps: With the help of the information elicitation factor and expect inspiration factor, with the help of the pheromone intensity design pheromone update strategy, with the help of three constraints to complete the optimization process. Experimental results show that the three constraint conditions ant colony scheduling algorithm can complete the resource scheduling in a shorter time, and the load distribution of the scheduling results is more balanced.

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“…By using the new technique, only legitimate users know the account name and password, so they can manipulate the data, no matter when and where customers are; they can obtain the adaptive sensing information as long as the readers which can be fixed or mobile are connected to the network [13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sensing Private Property Protection Protocol Based on Cloud

Fan

Wang

2015

International Journal of Distributed Sensor Networks

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Private property refers to something carrying private information, or expensive items; these items are very important for companies or individuals. The proposed private property protection system uses adaptive sensing technology to protect private materials in real time. In this paper, we propose an adaptive sensing private property authentication scheme which can be applied in the cloud computing. Considering a relatively safe room with a remote reader, there are several valuable items in the room. Each item is labeled with a unique tamper-evident adaptive sensor and the reader can simultaneously read a plurality of sensors. Encrypted information of items, sensors, and readers is stored in the cloud. The reader reads sensors and uploads the collected data to the cloud for further processing in real time. The proposed scheme is under the cloud environment to protect the user privacy and prevent synchronized attacks. Compared with some traditional schemes, our scheme is economical, practical, and easy to be expanded. Furthermore, it pays attention to privacy protection with real-time monitoring.

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A system for ubiquitous fall monitoring at home via a wireless sensor network and a wearable mote

Cited by 57 publications

References 13 publications

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Application of Ant Colony Algorithm in Cloud Resource Scheduling Based on Three Constraint Conditions

Adaptive Sensing Private Property Protection Protocol Based on Cloud

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