Activity Recognition System Using Non-intrusive Devices through a Complementary Technique Based on Discrete Methods

Concepción, Miguel Ángel Álvarez de la; Morillo, Luis Miguel Soria; Abril, Luis González; Ramírez, Juan Antonio Ortega

doi:10.1007/978-3-642-41043-7_4

Cited by 3 publications

(2 citation statements)

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“…The winner team of 2012 edition (USS team) [37], composed of three of the authors of this paper, obtained acceptable results in accuracy (it was below that of the CMUU team), but its simplicity (although it uses multiple mathematical methods it only relies on accelerometers) and interoperability allowed it to achieve good marks in all the evaluated criteria.…”

Section: Resultsmentioning

confidence: 88%

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Concepción

Morillo

Álvarez-García

et al. 2017

Pervasive and Mobile Computing

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a b s t r a c tCurrently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.

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Section: Resultsmentioning

confidence: 88%

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Concepción

Morillo

Álvarez-García

et al. 2017

Pervasive and Mobile Computing

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“…The results obtained in this phase are described in [21] and have been used to present the system to the EVAAL competition. After peer review, our team was accepted to the competition together with the teams: IJS (from the Jožef Stefan Institute of Ljubljana, Slovenia) [84], AmevaActivity (from the University of Seville, Spain) [85], and CUJ (from the University of Chiba, Japan) [86]. Competitors were invited to install and run their Activity Recognition System (ARS) during a predefined time slot.…”

Section: Fielding the System In A Real Smart Environmentmentioning

confidence: 99%

Human activity recognition using multisensor data fusion based on Reservoir Computing

Palumbo

Gallicchio

Pucci

et al. 2016

AIS

136

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Activity recognition plays a key role in providing activity assistance and care for users in smart homes. In this work, we present an activity recognition system that classifies in the near real-time a set of common daily activities exploiting both the data sampled by sensors embedded in a smartphone carried out by the user and the reciprocal Received Signal Strength (RSS) values coming from worn wireless sensor devices and from sensors deployed in the environment. In order to achieve an effective and responsive classification, a decision tree based on multisensor data-stream is applied fusing data coming from embedded sensors on the smartphone and environmental sensors before processing the RSS stream. To this end, we model the RSS stream, obtained from a Wireless Sensor Network (WSN), using Recurrent Neural Networks (RNNs) implemented as efficient Echo State Networks (ESNs), within the Reservoir Computing (RC) paradigm. We targeted the system for the EvAAL scenario, an international competition that aims at establishing benchmarks and evaluation metrics for comparing Ambient Assisted Living (AAL) solutions. In this paper, the performance of the proposed activity recognition system is assessed on a purposely collected real-world dataset, taking also into account a competitive neural network approach for performance comparison. Our results show that, with an appropriate configuration of the information fusion chain, the proposed system reaches a very good accuracy with a low deployment cost.

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Challenges in data collection in real-world environments for activity recognition

Lameski

Dimitrievski

Zdravevski

et al. 2019

IEEE EUROCON 2019 -18th International Conference on Smart Technologies

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Activity Recognition System Using Non-intrusive Devices through a Complementary Technique Based on Discrete Methods

Cited by 3 publications

References 20 publications

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Human activity recognition using multisensor data fusion based on Reservoir Computing

Challenges in data collection in real-world environments for activity recognition

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