A Finite State Machine-Based Fall Detection Mechanism on Smartphones
This paper presents a detection mechanism that utilizes the accelerometer in a smart phone carried by an individual to measure the human movement and hence determine if a fall event has occurred. The model of the fall activities are characterized as a finite state machine, which transits from one st...
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| creator | Shang-Lin Hsieh Ming Hsiung Su Lu Feng Liu Wey-Wen Jiang |
| description | This paper presents a detection mechanism that utilizes the accelerometer in a smart phone carried by an individual to measure the human movement and hence determine if a fall event has occurred. The model of the fall activities are characterized as a finite state machine, which transits from one state to another according to the data generated from the accelerometer. The presented detection mechanism utilizes the finite state machine to identify different types of falls, including forward falls, backward falls, and lateral falls. Experiments were conducted to evaluate the performance of the presented mechanism. The results show that the mechanism can effectively distinguish between actual fall events and normal activities such as squatting, and walking up and down stairs. |
| doi_str_mv | 10.1109/UIC-ATC.2012.153 |
| format | Conference Proceeding |
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The model of the fall activities are characterized as a finite state machine, which transits from one state to another according to the data generated from the accelerometer. The presented detection mechanism utilizes the finite state machine to identify different types of falls, including forward falls, backward falls, and lateral falls. Experiments were conducted to evaluate the performance of the presented mechanism. The results show that the mechanism can effectively distinguish between actual fall events and normal activities such as squatting, and walking up and down stairs.</description><identifier>ISBN: 1467330841</identifier><identifier>ISBN: 9781467330848</identifier><identifier>EISBN: 0769548431</identifier><identifier>EISBN: 9780769548432</identifier><identifier>DOI: 10.1109/UIC-ATC.2012.153</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>accelerometer ; Accelerometers ; Automata ; Conferences ; fall detecion ; finite state machine ; Injuries ; Legged locomotion ; Sensitivity ; Smart phones</subject><ispartof>2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing, 2012, p.735-739</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6332075$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6332075$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Shang-Lin Hsieh</creatorcontrib><creatorcontrib>Ming Hsiung Su</creatorcontrib><creatorcontrib>Lu Feng Liu</creatorcontrib><creatorcontrib>Wey-Wen Jiang</creatorcontrib><title>A Finite State Machine-Based Fall Detection Mechanism on Smartphones</title><title>2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing</title><addtitle>uic-atc</addtitle><description>This paper presents a detection mechanism that utilizes the accelerometer in a smart phone carried by an individual to measure the human movement and hence determine if a fall event has occurred. The model of the fall activities are characterized as a finite state machine, which transits from one state to another according to the data generated from the accelerometer. The presented detection mechanism utilizes the finite state machine to identify different types of falls, including forward falls, backward falls, and lateral falls. Experiments were conducted to evaluate the performance of the presented mechanism. The results show that the mechanism can effectively distinguish between actual fall events and normal activities such as squatting, and walking up and down stairs.</description><subject>accelerometer</subject><subject>Accelerometers</subject><subject>Automata</subject><subject>Conferences</subject><subject>fall detecion</subject><subject>finite state machine</subject><subject>Injuries</subject><subject>Legged locomotion</subject><subject>Sensitivity</subject><subject>Smart phones</subject><isbn>1467330841</isbn><isbn>9781467330848</isbn><isbn>0769548431</isbn><isbn>9780769548432</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotT01Lw0AUXBFBW3sXvOQPJO7btx_JsaZGCy0e2p7LfryQlTQt3Vz89wbsHGYYBoYZxl6AFwC8ejus63y5rwvBQRSg8I7NuNGVkqVEuGczkNog8lLCI1uk9MMnlEIJrZ7Yapk1cYgjZbvRTry1vosD5e82Ucga2_fZikbyYzwP2ZZ8Z4eYTtlkdid7HS_deaD0zB5a2yda3HTODs3Hvv7KN9-f63q5ySMYNeYekVyQAK4NFTnEoLVDKRxI4uCVC95qap0x1BoPggIRCeIkp5cULM7Z639vnILj5RqnCb9HjSi4UfgH-KZL0w</recordid><startdate>201209</startdate><enddate>201209</enddate><creator>Shang-Lin Hsieh</creator><creator>Ming Hsiung Su</creator><creator>Lu Feng Liu</creator><creator>Wey-Wen Jiang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201209</creationdate><title>A Finite State Machine-Based Fall Detection Mechanism on Smartphones</title><author>Shang-Lin Hsieh ; Ming Hsiung Su ; Lu Feng Liu ; Wey-Wen Jiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c33ebd411bfd9eb33d66b342b14e01c5bdca6efb77ef7c12edeee2e0e4109eda3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>accelerometer</topic><topic>Accelerometers</topic><topic>Automata</topic><topic>Conferences</topic><topic>fall detecion</topic><topic>finite state machine</topic><topic>Injuries</topic><topic>Legged locomotion</topic><topic>Sensitivity</topic><topic>Smart phones</topic><toplevel>online_resources</toplevel><creatorcontrib>Shang-Lin Hsieh</creatorcontrib><creatorcontrib>Ming Hsiung Su</creatorcontrib><creatorcontrib>Lu Feng Liu</creatorcontrib><creatorcontrib>Wey-Wen Jiang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shang-Lin Hsieh</au><au>Ming Hsiung Su</au><au>Lu Feng Liu</au><au>Wey-Wen Jiang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Finite State Machine-Based Fall Detection Mechanism on Smartphones</atitle><btitle>2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing</btitle><stitle>uic-atc</stitle><date>2012-09</date><risdate>2012</risdate><spage>735</spage><epage>739</epage><pages>735-739</pages><isbn>1467330841</isbn><isbn>9781467330848</isbn><eisbn>0769548431</eisbn><eisbn>9780769548432</eisbn><coden>IEEPAD</coden><abstract>This paper presents a detection mechanism that utilizes the accelerometer in a smart phone carried by an individual to measure the human movement and hence determine if a fall event has occurred. The model of the fall activities are characterized as a finite state machine, which transits from one state to another according to the data generated from the accelerometer. The presented detection mechanism utilizes the finite state machine to identify different types of falls, including forward falls, backward falls, and lateral falls. Experiments were conducted to evaluate the performance of the presented mechanism. The results show that the mechanism can effectively distinguish between actual fall events and normal activities such as squatting, and walking up and down stairs.</abstract><pub>IEEE</pub><doi>10.1109/UIC-ATC.2012.153</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing, 2012, p.735-739 |
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| language | eng |
| recordid | cdi_ieee_primary_6332075 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | accelerometer Accelerometers Automata Conferences fall detecion finite state machine Injuries Legged locomotion Sensitivity Smart phones |
| title | A Finite State Machine-Based Fall Detection Mechanism on Smartphones |
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