In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the possibility to discern the activities of different persons, across different time-spans and environments. To achieve this, we devise a new technique to clean and process the channel frequency response (CFR) phase of the Wi-Fi channel, obtaining an estimate of the Doppler shift at a radio monitor device. The Doppler shift reveals the presence of moving scatterers in the environment, while not being affected by (environment-specific) static objects. SHARP is trained on data collected as a person performs seven different activities in a single environment. It is then tested on different setups, to assess its performance as the person, the day and/or the environment change with respect to those considered at training time. In the worst-case scenario, it reaches an average accuracy higher than $95\%$, validating the effectiveness of the extracted Doppler information, used in conjunction with a learning algorithm based on a neural network, in recognizing human activities in a subject and environment independent way. The collected CFR dataset and the code are publicly available for replicability and benchmarking purposes [13].

Meneghello F., Garlisi D., Fabbro N.D., Tinnirello I., Rossi M. (2022). SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points. IEEE TRANSACTIONS ON MOBILE COMPUTING, 1-16 [10.1109/TMC.2022.3185681].

SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points

Garlisi D.
;
Tinnirello I.;
2022

Abstract

In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the possibility to discern the activities of different persons, across different time-spans and environments. To achieve this, we devise a new technique to clean and process the channel frequency response (CFR) phase of the Wi-Fi channel, obtaining an estimate of the Doppler shift at a radio monitor device. The Doppler shift reveals the presence of moving scatterers in the environment, while not being affected by (environment-specific) static objects. SHARP is trained on data collected as a person performs seven different activities in a single environment. It is then tested on different setups, to assess its performance as the person, the day and/or the environment change with respect to those considered at training time. In the worst-case scenario, it reaches an average accuracy higher than $95\%$, validating the effectiveness of the extracted Doppler information, used in conjunction with a learning algorithm based on a neural network, in recognizing human activities in a subject and environment independent way. The collected CFR dataset and the code are publicly available for replicability and benchmarking purposes [13].
Meneghello F., Garlisi D., Fabbro N.D., Tinnirello I., Rossi M. (2022). SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points. IEEE TRANSACTIONS ON MOBILE COMPUTING, 1-16 [10.1109/TMC.2022.3185681].
File in questo prodotto:
File Dimensione Formato  
SHARP_Environment_and_Person_Independent_Activity_Recognition_with_Commodity_IEEE_802.11_Access_Points.pdf

Solo gestori archvio

Tipologia: Pre-print
Dimensione 6.01 MB
Formato Adobe PDF
6.01 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

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: http://hdl.handle.net/10447/567944
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact