Abstract
Fall detection systems are relevant in our aging society aiming to support efforts towards reducing the impact of accidental falls. However, current solutions lack the ability to combine low-power consumption, privacy protection, low latency response, and low payload. In this work, we address this gap through a comparative analysis of the trade-off between effectiveness and energy consumption by comparing a Recurrent Spiking Neural Network (RSNN) with a Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN). By leveraging two pre-existing RGB datasets and an event-camera simulator, we generated event data by converting intensity frames into event streams. Thus, we could harness the salient features of event-based data and analyze their benefits when combined with RSNNs and LSTMs. The compared approaches are evaluated on two data sets collected from a single subject; one from a camera attached to the neck (N-data) and the other one attached to the waist (W-data). Each data set contains 469 video samples, of which 213 are four types of fall examples, and the rest are nine types of non-fall daily activities. Compared to the CNN, which operates on the high-resolution RGB frames, the RSNN requires 200× less trainable parameters. However, the CNN outperforms the RSNN by 23.7 and 17.1% points for W- and N-data, respectively. Compared to the LSTM, which operates on event-based input, the RSNN requires 5× less trainable parameters and 2000× less MAC operations while exhibiting a 1.9 and 8.7% points decrease in accuracy for W- and N-data, respectively. Overall, our results show that the event-based data preserves enough information to detect falls. Our work paves the way to the realization of high-energy efficient fall detection systems.
Original language | English |
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Title of host publication | Computer Analysis of Images and Patterns |
Subtitle of host publication | 20th International Conference, CAIP 2023 Limassol, Cyprus, September 25–28, 2023 Proceedings, Part II |
Editors | Nicolas Tsapatsoulis |
Publisher | Springer |
Pages | 33-42 |
Number of pages | 10 |
ISBN (Electronic) | 978-3-031-44240-7 |
ISBN (Print) | 978-3-031-44239-1 |
DOIs | |
Publication status | Published - 20-Sept-2023 |
Event | 20th International Conference on Computer Analysis of Images and Patterns : CAIP2023 - Limassol, Cyprus Duration: 25-Sept-2023 → 28-Sept-2023 https://cyprusconferences.org/caip2023/ |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 14185 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 20th International Conference on Computer Analysis of Images and Patterns : CAIP2023 |
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Country/Territory | Cyprus |
City | Limassol |
Period | 25/09/2023 → 28/09/2023 |
Internet address |