Many applications aim to make smarter the indoor environments where most people spend much of their time (home, office, transportation, public spaces), but they need long-term low-cost human sensing and monitoring capabilities. Small capacitive sensors match well most requirements, like privacy, power, cost, and unobtrusiveness, and, importantly, they do not rely on wearables or specific human interactions. However, long-range capacitive sensors often need advanced data processing to increase their performance. Our ongoing research experimental results show that four 16 cm X 16 cm capacitive sensors deployed in a 3 m X 3 m room can tag-lessly track the movement of a person with a root mean square error as low as 26 cm. Our system uses a median and low-pass filter for sensor signal conditioning before an autoregressive neural network that we trained to infer the location of the person in the room.

Neural network-based indoor tag-less localization using capacitive sensors / Tariq, Osama Bin; Lazarescu, Mihai Teodor; Lavagno, Luciano. - ELETTRONICO. - (2019), pp. 9-12. ((Intervento presentato al convegno 21st International Conference on Ubiquitous and Pervasive Computing (Ubicomp) 2019 and the International Symposium on Wearable Computing (ISWC) 2019 tenutosi a London, United Kingdom nel September 09 - 13, 2019 [10.1145/3341162.3343838].

Neural network-based indoor tag-less localization using capacitive sensors

Tariq, Osama Bin;Lazarescu, Mihai Teodor;Lavagno, Luciano
2019

Abstract

Many applications aim to make smarter the indoor environments where most people spend much of their time (home, office, transportation, public spaces), but they need long-term low-cost human sensing and monitoring capabilities. Small capacitive sensors match well most requirements, like privacy, power, cost, and unobtrusiveness, and, importantly, they do not rely on wearables or specific human interactions. However, long-range capacitive sensors often need advanced data processing to increase their performance. Our ongoing research experimental results show that four 16 cm X 16 cm capacitive sensors deployed in a 3 m X 3 m room can tag-lessly track the movement of a person with a root mean square error as low as 26 cm. Our system uses a median and low-pass filter for sensor signal conditioning before an autoregressive neural network that we trained to infer the location of the person in the room.
File in questo prodotto:
File Dimensione Formato  
p9-bin_tariq.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 797.64 kB
Formato Adobe PDF
797.64 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
ACM_Conference_Proceedings__Master__Template_pre_publication.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 880.15 kB
Formato Adobe PDF
880.15 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2752352
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo