Multiple Biopotentials Acquisition System for Wearable Applications

Benatti, Simone; Milosevic, Bojan; Tomasini, Marco; Farella, Elisabetta; Schoenle, Philipp; Bunjaku, Petrit; Rovere, Giovanni; Fateh, Schekeb; Huang, Qiuting; Benini, Luca

doi:10.5220/0005320302600268

Cited by 13 publications

(10 citation statements)

References 20 publications

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“…The wearable platform is designed for medical IoT applications and derived from [46] and [47]. The system is composed of an active EEG sensor array interfaced to a custom board with a biopotential ADC and a low power microcontroller with DSP capabilities.…”

Section: Iiimaterials and Methodsmentioning

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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Section: Iiimaterials and Methodsmentioning

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.

Self Cite

View full text Add to dashboard Cite

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“…Hence, the frame duration is set at = 2 ms; i.e., the time between each data point for the ECG and the rest of the biopotential signals is also set at 2 ms. Note that by assuring a high sampling rate, the rest of the signals are oversampled, which allows the correct reconstruction of all signals in the sink node according to the Nyquist theorem [36].…”

Section: System Designmentioning

confidence: 99%

Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

Chávez

Rivero-Ángeles

Jiménez

et al. 2019

Wireless Communications and Mobile Computing

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Wireless sensor networks have recently been used to monitor bioelectric signals generated by the human body. However, since the dimensions and capabilities of the network nodes are small and limited, a problem is generated inherently by the energy consumption and radioelectric interference. In view of this, we propose two priority schemes to improve the performance of the network. In the first one, we aim at increasing the system lifetime by reducing the number of transmissions of nodes with low energy levels. In the second scheme, we aim at conveying priority data to the sink node as fast and efficient as possible. In the former scheme, users can be monitored for long time periods considering a low data generation. On the other hand, the latter scheme allows expediting transmission of important data when energy efficiency is not relevant. In this article, we mathematically model, analyze, and study the performance of the system for both priority schemes considering cognitive radio capabilities to make efficient use of resources and limit the radioelectric interference when the network transmits both continuous monitoring and event detection information. Numerical results provided in this work allow a careful parameter selection for practical implementation of BANETs.

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“…The global Human-Machine Interface (HMI) market is expected to generate revenues of more than 8 billion USD over the next 5 years. This trend is driven by the increasing adoption of devices for industrial automation [1], wearable health tracking [2] and, more in general, the growing plethora of IoT ecosystems.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient IoT node for HMI applications based on an ultra-low power Multicore Processor

Kartsch

Guermandi

Benatti

et al. 2019

2019 IEEE Sensors Applications Symposium (SAS)

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Developing wearable sensing technologies and unobtrusive devices is paving the way to the design of compelling applications for the next generation of systems for a smart IoT node for Human Machine Interaction (HMI). In this paper we present a smart sensor node for IoT and HMI based on a programmable Parallel Ultra-Low-Power (PULP) platform. We tested the system on a hand gesture recognition application, which is a preferred way of interaction in HMI design. A wearable armband with 8 EMG sensors is controlled by our IoT node, running a machine learning algorithm in real-time, recognizing up to 11 gestures with a power envelope of 11.84 mW. As a result, the proposed approach is capable to 35 hours of continuous operation and 1000 hours in standby. The resulting platform minimizes effectively the power required to run the software application and thus, it allows more power budget for high-quality AFE.

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Multiple Biopotentials Acquisition System for Wearable Applications

Cited by 13 publications

References 20 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

Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

An Energy-Efficient IoT node for HMI applications based on an ultra-low power Multicore Processor

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