{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T11:30:03Z","timestamp":1783251003276,"version":"3.54.6"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T00:00:00Z","timestamp":1515369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"United Technologies Research Centre, Cork, Ireland"},{"DOI":"10.13039\/501100002081","name":"Irish Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002081","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,1,8]]},"abstract":"<jats:p>Falling or tripping among elderly people living on their own is recognized as a major public health worry that can even lead to death. Fall detection systems that alert caregivers, family members or neighbours can potentially save lives. In the past decade, an extensive amount of research has been carried out to develop fall detection systems based on a range of different detection approaches, i.e, wearable and non-wearable sensing and detection technologies. In this paper, we consider an emerging non-wearable fall detection approach based on WiFi Channel State Information (CSI). Previous CSI based fall detection solutions have considered only time domain approaches. Here, we take an altogether different direction, time-frequency analysis as used in radar fall detection. We use the conventional Short-Time Fourier Transform (STFT) to extract time-frequency features and a sequential forward selection algorithm to single out features that are resilient to environment changes while maintaining a higher fall detection rate. When our system is pre-trained, it has a 93% accuracy and compared to RTFall and CARM, this is a 12% and 15% improvement respectively. When the environment changes, our system still has an average accuracy close to 80% which is more than a 20% to 30% and 5% to 15% improvement respectively.<\/jats:p>","DOI":"10.1145\/3161183","type":"journal-article","created":{"date-parts":[[2018,1,9]],"date-time":"2018-01-09T13:26:11Z","timestamp":1515504371000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":261,"title":["FallDeFi"],"prefix":"10.1145","volume":"1","author":[{"given":"Sameera","family":"Palipana","sequence":"first","affiliation":[{"name":"Nimbus Centre Cork Institute of Technology, Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Rojas","sequence":"additional","affiliation":[{"name":"Nimbus Centre Cork Institute of Technology, Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piyush","family":"Agrawal","sequence":"additional","affiliation":[{"name":"United Technologies Research Centre, Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Pesch","sequence":"additional","affiliation":[{"name":"Nimbus Centre Cork Institute of Technology, Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,1,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"United Nations Department of Economic and Social Affairs Population Division (2015). World Population Prospects: The 2015 Revision custom data acquired via website. https:\/\/esa.un.org\/unpd\/wpp\/. Accessed: 2017-04-24.  United Nations Department of Economic and Social Affairs Population Division (2015). World Population Prospects: The 2015 Revision custom data acquired via website. https:\/\/esa.un.org\/unpd\/wpp\/. Accessed: 2017-04-24."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2016.7869686"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2015.2502784"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2367038"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2017.2697077"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1136\/ip.9.1.93-a"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370292"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MASS.2012.6502524"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2016.2557795"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.09.037"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/1475-925X-12-66"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/s140712900"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2015.2504935"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790093"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2013.6666749"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2015.7218525"},{"key":"e_1_2_1_17_1","first-page":"1","volume-title":"IEEE 24th Int. Conf. on Network Protocols (ICNP)","author":"Chi Zicheng","year":"2016"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980115.2980128"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2009.2012849"},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Ph Van Dorp and FCA Groen. Feature-based human motion parameter estimation with radar. IET Radar Sonar 8 Navigation 2(2):135--145 2008.  Ph Van Dorp and FCA Groen. Feature-based human motion parameter estimation with radar. IET Radar Sonar 8 Navigation 2(2):135--145 2008.","DOI":"10.1049\/iet-rsn:20070086"},{"key":"e_1_2_1_21_1","first-page":"907712","volume-title":"SPIE Defense+ Security","author":"Gadde Ajay","year":"2014"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ChinaSIP.2014.6889337"},{"key":"e_1_2_1_23_1","volume-title":"Signal Processing Conference (EUSIPCO), 2016","author":"Erol Baris","year":"2075"},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Qisong Wu Yimin D Zhang Wenbing Tao and Moeness G Amin. Radar-based fall detection based on Doppler time--frequency signatures for assisted living. IET Radar Sonar 8 Navigation 9(2):164--172 2015.  Qisong Wu Yimin D Zhang Wenbing Tao and Moeness G Amin. Radar-based fall detection based on Doppler time--frequency signatures for assisted living. IET Radar Sonar 8 Navigation 9(2):164--172 2015.","DOI":"10.1049\/iet-rsn.2014.0250"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2013.6666753"},{"key":"e_1_2_1_26_1","first-page":"617","volume-title":"Proc. of the 20th annual int. conf. on Mobile computing and networking (Mobicom)","author":"Yan","year":"2014"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971670"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3025678"},{"key":"e_1_2_1_29_1","volume-title":"IEEE INFOCOM 2014 - IEEE Conference on Computer Communications","author":"Wifall C. Han","year":"2014"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2016.2557792"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1851275.1851203"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2993422.2993579"},{"key":"e_1_2_1_33_1","unstructured":"Jonathon Shlens. A tutorial on principal component analysis. CoRR abs\/1404.1100 2014.  Jonathon Shlens. A tutorial on principal component analysis. CoRR abs\/1404.1100 2014."},{"key":"e_1_2_1_34_1","first-page":"982918","volume-title":"SPIE Defense+ Security","author":"Erol Baris","year":"2016"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2007.902249"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2008.2001068"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2008.12.011"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/BMEI.2008.254"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1925861.1925870"},{"key":"e_1_2_1_40_1","unstructured":"Sameera Palipana. Falldefi source code and data. https:\/\/github.com\/dmsp123\/FallDeFi 2017.  Sameera Palipana. Falldefi source code and data. https:\/\/github.com\/dmsp123\/FallDeFi 2017."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/18.382009"},{"key":"e_1_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Nitesh V Chawla Kevin W Bowyer Lawrence O Hall and W Philip Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16:321--357 2002.  Nitesh V Chawla Kevin W Bowyer Lawrence O Hall and W Philip Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16:321--357 2002.","DOI":"10.1613\/jair.953"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3161183","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3161183","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:13:30Z","timestamp":1750212810000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3161183"}},"subtitle":["Ubiquitous Fall Detection using Commodity Wi-Fi Devices"],"short-title":[],"issued":{"date-parts":[[2018,1,8]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,1,8]]}},"alternative-id":["10.1145\/3161183"],"URL":"https:\/\/doi.org\/10.1145\/3161183","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,8]]},"assertion":[{"value":"2017-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-10-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-01-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}