Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Martín, Henar; Iglesias, Josué; Cano, Jesus; Bernardos, Ana M.; Casar, José R.

doi:10.1109/percomw.2012.6197634

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…The overall system activity detection and identification accuracy was 95.5%, while 2.5% of activities were recognized incorrectly, and 2.0% were not detected at all. The achieved accuracies do not differ from those reported in [27,30].…”

Section: Form Of Activity Recognitionmentioning

confidence: 44%

IoT-Based Information System for Healthcare Application: Design Methodology Approach

Dziak

Jachimczyk²,

Kulesza

2017

Applied Sciences

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Abstract:Over the last few decades, life expectancy has increased significantly. However, elderly people who live on their own often need assistance due to mobility difficulties, symptoms of dementia or other health problems. In such cases, an autonomous supporting system may be helpful. This paper proposes the Internet of Things (IoT)-based information system for indoor and outdoor use. Since the conducted survey of related works indicated a lack of methodological approaches to the design process, therefore a Design Methodology (DM), which approaches the design target from the perspective of the stakeholders, contracting authorities and potential users, is introduced. The implemented solution applies the three-axial accelerometer and magnetometer, Pedestrian Dead Reckoning (PDR), thresholding and the decision trees algorithm. Such an architecture enables the localization of a monitored person within four room-zones with accuracy; furthermore, it identifies falls and the activities of lying, standing, sitting and walking. Based on the identified activities, the system classifies current activities as normal, suspicious or dangerous, which is used to notify the healthcare staff about possible problems. The real-life scenarios validated the high robustness of the proposed solution. Moreover, the test results satisfied both stakeholders and future users and ensured further cooperation with the project.

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Section: Form Of Activity Recognitionmentioning

confidence: 44%

IoT-Based Information System for Healthcare Application: Design Methodology Approach

Dziak

Jachimczyk²,

Kulesza

2017

Applied Sciences

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“…Energy harvesting [23,43,44,45,46,47] Wireless charging [48,18] Sensor set selection [49,50,51,5,52] Deactivate power hungry sensor [42,6,4,20,28,53,54] Context-based pull services [55,33,28] Adaptive sampling rate [7,30,56] On-board computation [8,57,6] Network interface selection [58,59,60,61,62] Computation offloading [24,29,63,64] Opportunistic resources sharing [22,65,26,27] Feature subset selection [66,30] Adaptive classifier selection [67,68,69] Adaptive classifier operations [32,…”

Section: Computation Reductionmentioning

confidence: 99%

“…Martin et al [67] developed a fuzzy-based on-line classifier selection that selects the best-suited classifier given i) offline classifier performance evaluation (trained accuracy, delay, memory need, complexity) ii) online accuracy and delay requirements (low, medium, high) and iii) the current device state (battery level, available memory, CPU load). In practice, given the current application requirements and device state, the fuzzy selector outputs a score indicating the desired quality level of the classification (i.e the desired accuracy, response time, complexity and memory use of an ideal classifier).…”

Section: Adaptive Classifier Selectionmentioning

confidence: 99%

A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications

Rault

Bouabdallah

Challal

et al. 2017

Pervasive and Mobile Computing

103

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Human context recognition (HCR) from on-body sensor networks is an important and challenging task for many healthcare applications because it offers continuous monitoring capability of both personal and environmental parameters. However, these systems still face a major energy issue that prevent their wide adoption. Indeed, in healthcare applications, sensors are used to capture data during daily life or extended stays in hospital. Thus, continuous sampling and communication tasks quickly deplete sensors' battery reserves, and frequent battery replacement are not convenient. Therefore, there is a need to develop energyefficient solutions for long-term monitoring applications in order to foster the acceptance of these technologies by the patients. In this paper, we survey existing energy-efficient approaches designed for HCR based on wearable sensor networks. We propose a new classification of the energy-efficient mechanisms for healthrelated human context recognition applications and we review the related works in details. Moreover, we provide a qualitative comparison of these solutions in terms of energy-consumption, recognition accuracy and latency. Finally, we discuss open research issue and give directions for future works.

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“…Using such system they recognize 25 different activities by matching current accelerometer data with the pre-defined activity set and identified RFID tags. M. Henar, et al [29] use a Google Nexus S accelerometer, gyroscope, magnetometer, light and proximity sensor applied to fuzzy classifier to recognise body position and other activities.…”

Section: Iirelated Workmentioning

confidence: 99%

Wirelessly Interfacing Objects and Subjects of Healthcare System – IoT Approach

Dziak¹,

Jachimczyk²,

Kulesza³

2016

ElAEE

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Wireless sensor networks, WSN, for which development has begun by military applications, are nowadays applied to all human activities; e.g. in medicine for patience monitoring or to reduce the effects of disasters. Therefore, the WSNs area has been also one of the emerging and fast growing scientific fields. Increasing interest of WSNs is even caused by equally intense growth of interest in the Internet of Things domain, IoT, in which WSNs constitute a significant part. These reasons have brought about developing low cost, lowpower and multi-function sensor nodes. However, the major fact that sensor nodes run quickly out of energy has been an issue and many energy efficient routing protocols have been proposed to solve this problem. Case study presented in this paper concern design of WSN in IoT concept from system lifetime perspective. A hierarchical routing technique, which shows energy efficiency, has been validated. Simulation results show that chosen technique prolongs the lifetime of the WSN compared to other investigated clustering schemes. The advantages of this method are validated by comparative studies.

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Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Cited by 3 publications

References 14 publications

IoT-Based Information System for Healthcare Application: Design Methodology Approach

IoT-Based Information System for Healthcare Application: Design Methodology Approach

A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications

Wirelessly Interfacing Objects and Subjects of Healthcare System – IoT Approach

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