Wearable sensors and Internet of Things technologies are enabling automated health monitoring applications, where signals captured by sensors are analyzed in real-time by algorithms detecting health issues and conditions. However, continuous clinical-level monitoring of patients in everyday settings often requires computation, storage and connectivity capabilities beyond those possessed by wearable sensors. While edge computing partially resolves this issue by connecting the sensors to compute-capable devices positioned at the network edge, the wireless links connecting the sensors to the edge servers may not have sufficient capacity to transfer the information-rich data that characterize these applications. A possible solution is to compress the signal to be transferred, accepting the tradeoff between compression gain and detection accuracy. In this paper, we propose SIC-EDGE: a "semantic compression"framework whose goal is to dynamically optimize the resolution of an electrocardiogram (ECG) signal transferred from a wearable sensor to an edge server to perform real-time detection of heart diseases. The core idea is to establish a collaborative control loop between the sensor and the edge server to iteratively build a semantic representation that is: (i) ECG-cycle specific; (ii) personalized, and (iii) targeted to support the classification task rather than signal reconstruction. The core of SIC-EDGE is a Sequential Hypothesis Testing (SHT) algorithm that analyzes partial representations along the iterations to determine which and how many representation layers (wavelet coefficients in our implementation) are requested. Our results on established datasets demonstrates the need for adaptive "semantic"compression, and illustrate the dynamic compression strategy realized by SIC-EDGE. We show that SIC-EDGE leads to an increase in terms of recall and F1 score of up to 35% and 26% respectively compared to an optimized but static wavelet compression for a given maximum channel usage.

SIC-EDGE: Semantic Iterative ECG Compression for Edge-Assisted Wearable Systems / Amiri, D.; Takalo-Mattila, J.; Bedogni, L.; Levorato, M.; Dutt, N.. - (2022), pp. 377-385. ((Intervento presentato al convegno 23rd IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2022 tenutosi a gbr nel 2022 [10.1109/WoWMoM54355.2022.00036].

SIC-EDGE: Semantic Iterative ECG Compression for Edge-Assisted Wearable Systems

Bedogni L.;
2022-01-01

Abstract

Wearable sensors and Internet of Things technologies are enabling automated health monitoring applications, where signals captured by sensors are analyzed in real-time by algorithms detecting health issues and conditions. However, continuous clinical-level monitoring of patients in everyday settings often requires computation, storage and connectivity capabilities beyond those possessed by wearable sensors. While edge computing partially resolves this issue by connecting the sensors to compute-capable devices positioned at the network edge, the wireless links connecting the sensors to the edge servers may not have sufficient capacity to transfer the information-rich data that characterize these applications. A possible solution is to compress the signal to be transferred, accepting the tradeoff between compression gain and detection accuracy. In this paper, we propose SIC-EDGE: a "semantic compression"framework whose goal is to dynamically optimize the resolution of an electrocardiogram (ECG) signal transferred from a wearable sensor to an edge server to perform real-time detection of heart diseases. The core idea is to establish a collaborative control loop between the sensor and the edge server to iteratively build a semantic representation that is: (i) ECG-cycle specific; (ii) personalized, and (iii) targeted to support the classification task rather than signal reconstruction. The core of SIC-EDGE is a Sequential Hypothesis Testing (SHT) algorithm that analyzes partial representations along the iterations to determine which and how many representation layers (wavelet coefficients in our implementation) are requested. Our results on established datasets demonstrates the need for adaptive "semantic"compression, and illustrate the dynamic compression strategy realized by SIC-EDGE. We show that SIC-EDGE leads to an increase in terms of recall and F1 score of up to 35% and 26% respectively compared to an optimized but static wavelet compression for a given maximum channel usage.
23rd IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2022
gbr
2022
377
385
Amiri, D.; Takalo-Mattila, J.; Bedogni, L.; Levorato, M.; Dutt, N.
SIC-EDGE: Semantic Iterative ECG Compression for Edge-Assisted Wearable Systems / Amiri, D.; Takalo-Mattila, J.; Bedogni, L.; Levorato, M.; Dutt, N.. - (2022), pp. 377-385. ((Intervento presentato al convegno 23rd IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2022 tenutosi a gbr nel 2022 [10.1109/WoWMoM54355.2022.00036].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1288431
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