Human Activity Recognition (HAR) plays a prominent role in various domains, such as healthcare, surveillance, and sports. In this paper, our goal is to identify the most accurate Deep Learning (DL) algorithm under tiny deployability constraints. Our results show that a Recurrent Neural Network (RNN) given by the combination of a one-dimensional Convolutional Neural Network (ID-CNN) with Bi-directional Gated Recurrent Unit (Bi-GRU) is the most attractive solution, with respect to Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the recently introduced Legendre Memory Unit (LMU). The algorithm performance is investigated over a publicly available dataset consisting of 19 different daily activities. According to the obtained results, 1D-CNN-BiGRU has an average accuracy within 0.2% of that of BiGRU (the RNN with highest accuracy) with an execution time more than 4 times shorter.

Accurate Classification of Sport Activities Under Tiny Deployability Constraints / Mazinani, Armin; Davoli, Luca; Pau, Danilo Pietro; Ferrari, Gianluigi. - (2023), pp. 261-267. (Intervento presentato al convegno 2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) tenutosi a Bali, Indonesia) [10.1109/IoTaIS60147.2023.10346056].

Accurate Classification of Sport Activities Under Tiny Deployability Constraints

Mazinani, Armin;Davoli, Luca;Ferrari, Gianluigi
2023-01-01

Abstract

Human Activity Recognition (HAR) plays a prominent role in various domains, such as healthcare, surveillance, and sports. In this paper, our goal is to identify the most accurate Deep Learning (DL) algorithm under tiny deployability constraints. Our results show that a Recurrent Neural Network (RNN) given by the combination of a one-dimensional Convolutional Neural Network (ID-CNN) with Bi-directional Gated Recurrent Unit (Bi-GRU) is the most attractive solution, with respect to Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the recently introduced Legendre Memory Unit (LMU). The algorithm performance is investigated over a publicly available dataset consisting of 19 different daily activities. According to the obtained results, 1D-CNN-BiGRU has an average accuracy within 0.2% of that of BiGRU (the RNN with highest accuracy) with an execution time more than 4 times shorter.
2023
979-8-3503-1904-0
Accurate Classification of Sport Activities Under Tiny Deployability Constraints / Mazinani, Armin; Davoli, Luca; Pau, Danilo Pietro; Ferrari, Gianluigi. - (2023), pp. 261-267. (Intervento presentato al convegno 2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) tenutosi a Bali, Indonesia) [10.1109/IoTaIS60147.2023.10346056].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/11381/2966672
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact