Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals

Hooshmand, Mohsen; Zordan, Davide; Testa, Davide Del; Grisan, Enrico; Rossi, Michele

doi:10.1109/jiot.2017.2689164

Cited by 75 publications

(28 citation statements)

References 67 publications

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“…Moreover, it causes a severe reduction of the energy efficiency of the whole system [14], [15], [16] as it requires transmission of non-negligible amounts of data. To avoid these issues, the digital processing should be moved near-sensor, executing algorithms directly on the wearable device [17], [18]. However, such solutions are not readily available, both in commercial and research systems.…”

mentioning

confidence: 99%

A Minimally Invasive Low-Power Platform for Real-Time Brain Computer Interaction Based on Canonical Correlation Analysis

Salvaro

Benatti

Kartsch

et al. 2019

IEEE Internet Things J.

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A growing trend in Human Computer Interaction (HCI) is to integrate computational capabilities into wearable devices, to enable sophisticated and natural interaction modalities. Acting directly by decoding neural activity is a very natural way of interaction and one of the fundamental paradigms of Brain Computer Interfaces (BCIs) as well. In this work we present a wearable IoT node designed for BCI spelling. The system is based on Visual Evoked Potentials detection and runs the Canonical Correlation Analysis (CCA) on a low power microcontroller. Neural data is acquired by an array of EEG active dry electrodes, suitable for a minimally intrusive interface. To evaluate our solution, we optimized the system on eight subjects and tested it on five different subjects for four and eight stimuli, reaching a peak transfer rate of 1.57 bps, comparable with those achieved by state-of-the-art non-embedded systems. The power consumption of the device is less than 30 mW, resulting in 122 hours of operation with a standard 1000 mAh battery. I. INTRODUCTION Brain Computer Interfaces (BCIs) for Human-Computer Interaction (HCI) were first developed to support people with disabilities in their interaction with the external world, with one of the first successful examples being BCI spellers. Recent years have seen BCI applications reach out to a larger set of scenarios, such as industry, gaming, learning, healthcare [1] and rehabilitation [2]. Several tech companies developing consumer-oriented products (Google, Apple, Facebook, etc.) have also become active in this field [3], [4], [5], with the vision of being able to substitute traditional HCIs based on conventional computer input devices, gesture and voice recognition, touch-screen interaction [6], [7], [8], with the possibility to directly interact and control computers with our brain signals. Bringing this fascinating idea into life will be a tremendous boost towards integrating actions and interactions with objects in a fully-connected IoT scenario. Applications can range from verifying whether a worker is attending a specific task or effectively receiving a communication for safety purposes, to remotely control devices in industrial or home

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mentioning

confidence: 99%

A Minimally Invasive Low-Power Platform for Real-Time Brain Computer Interaction Based on Canonical Correlation Analysis

Salvaro

Benatti

Kartsch

et al. 2019

IEEE Internet Things J.

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“…Tested on a single subject, limited gesture recognition supported [54] A mobile real-time health monitoring architecture based on a heterogeneous multicore platform for ECG signal processing Enhanced battery life, low latency, low power device design Sub-optimal performance for clinical-grade signals due to frequent transmissions [55] A dictionary-based lossy signal compression technique for enhancing energy efficiency of wearables…”

Section: B Activity Recognition Applicationsmentioning

confidence: 99%

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

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Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged-therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables' energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.

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“…A combined compression algorithm [4] provided different biomedical signals that showed a significant improvement in the compression ratio with a small maximum error for optimizing the network resource usage over Wireless Sensor Networks (WSN). A lossy compression algorithm based on online dictionaries provided a quantitative assessment for compression, reconstruction and energy consumption of wearable Internet of Things (IoT) was presented in [11].…”

Section: Literature Reviewmentioning

confidence: 99%

VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network

Chen

Abu

et al. 2018

Applied Sciences

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Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity.

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Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals

Cited by 75 publications

References 67 publications

A Minimally Invasive Low-Power Platform for Real-Time Brain Computer Interaction Based on Canonical Correlation Analysis

A Minimally Invasive Low-Power Platform for Real-Time Brain Computer Interaction Based on Canonical Correlation Analysis

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network

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