Low intrusive ehealth monitoring: Human posture and activity level detection with an intelligent furniture network

Tapio Heikkilä, Esko Strömmer, S. Kivikunnas, Markku Järviluoma, Marko Korkalainen, Vesa Kyllönen, Esa-Matti Sarjanoja, Irina Peltomaa

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)

Abstract

Assisting elderly people living at home is a topical issue for Information and Communication Technology (ICT) developers. The motivation is in tracking the resident behavior and detecting abnormal living patterns. We take an approach for such an eHealth monitoring by an intelligent furniture network. Human behavior in the form of postures and activity levels is monitored using a set of intelligent furniture with very low cost low-intrusive capacitive proximity sensors. The sensor system relies on wireless sensor network technologies and is extended with data management and monitoring user interfaces via the internet. Our experimental tests show that compact algorithms based on nearest neighborhood classifiers and filter banks with Infinite Impulse Response (IIR) filters or Haar wavelets can identify the state of the furniture user in the form of postures and activity levels. Changes in posture and activity patterns can reveal behavioral anomalies, like restlessness and wandering, indicating possible health related unrevealed complications.
Original languageEnglish
Pages (from-to)57-63
Number of pages6
JournalIEEE Wireless Communications
Volume20
Issue number4
DOIs
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed

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Proximity sensors
Capacitive sensors
IIR filters
Monitoring
Filter banks
Information management
User interfaces
Wireless sensor networks
Classifiers
Health
Internet
Communication
Sensors
Costs

Cite this

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title = "Low intrusive ehealth monitoring: Human posture and activity level detection with an intelligent furniture network",
abstract = "Assisting elderly people living at home is a topical issue for Information and Communication Technology (ICT) developers. The motivation is in tracking the resident behavior and detecting abnormal living patterns. We take an approach for such an eHealth monitoring by an intelligent furniture network. Human behavior in the form of postures and activity levels is monitored using a set of intelligent furniture with very low cost low-intrusive capacitive proximity sensors. The sensor system relies on wireless sensor network technologies and is extended with data management and monitoring user interfaces via the internet. Our experimental tests show that compact algorithms based on nearest neighborhood classifiers and filter banks with Infinite Impulse Response (IIR) filters or Haar wavelets can identify the state of the furniture user in the form of postures and activity levels. Changes in posture and activity patterns can reveal behavioral anomalies, like restlessness and wandering, indicating possible health related unrevealed complications.",
author = "Tapio Heikkil{\"a} and Esko Str{\"o}mmer and S. Kivikunnas and Markku J{\"a}rviluoma and Marko Korkalainen and Vesa Kyll{\"o}nen and Esa-Matti Sarjanoja and Irina Peltomaa",
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Low intrusive ehealth monitoring : Human posture and activity level detection with an intelligent furniture network. / Heikkilä, Tapio; Strömmer, Esko; Kivikunnas, S.; Järviluoma, Markku; Korkalainen, Marko; Kyllönen, Vesa; Sarjanoja, Esa-Matti; Peltomaa, Irina.

In: IEEE Wireless Communications, Vol. 20, No. 4, 2013, p. 57-63.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

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T2 - Human posture and activity level detection with an intelligent furniture network

AU - Heikkilä, Tapio

AU - Strömmer, Esko

AU - Kivikunnas, S.

AU - Järviluoma, Markku

AU - Korkalainen, Marko

AU - Kyllönen, Vesa

AU - Sarjanoja, Esa-Matti

AU - Peltomaa, Irina

PY - 2013

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AB - Assisting elderly people living at home is a topical issue for Information and Communication Technology (ICT) developers. The motivation is in tracking the resident behavior and detecting abnormal living patterns. We take an approach for such an eHealth monitoring by an intelligent furniture network. Human behavior in the form of postures and activity levels is monitored using a set of intelligent furniture with very low cost low-intrusive capacitive proximity sensors. The sensor system relies on wireless sensor network technologies and is extended with data management and monitoring user interfaces via the internet. Our experimental tests show that compact algorithms based on nearest neighborhood classifiers and filter banks with Infinite Impulse Response (IIR) filters or Haar wavelets can identify the state of the furniture user in the form of postures and activity levels. Changes in posture and activity patterns can reveal behavioral anomalies, like restlessness and wandering, indicating possible health related unrevealed complications.

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