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

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

BioWolf16: a 16-channel, 24-bit, 4kSPS Ultra-Low Power Platform for Wearable Clinical-grade Bio-potential Parallel Processing and Streaming

Low-power wearable systems are essential for medical and industrial applications, but they face crucial implementation challenges when providing energy-efficient compact design while increasing the number of available channels, sampling rate and overall processing power. This work presents a small (39×41mm) wireless embedded low-power HMI device for ExG signals, offering up to 16 channels sampled at up to 4kSPS. By virtue of the high sampling rate and medical-grade signal quality (i.e. compliant with the IFCN standards), BioWolf16 is capable of accurate gesture recognition and enables the possibility to acquire data for neural spikes extraction. When employed over an EMG gesture recognition paradigm, the system achieves 90.24% classification accuracy over nine gestures (16 channels@4kSPS) while requiring only 16mW of power (57h of continuous operation) when deployed on Mr. Wolf MCU, part of the system architecture. The system can also provide up to 14h of real-time data streaming (4kSPS), which can further be extended to 23h when reducing the sampling rate to 1kSPS. Our results also demonstrate that this design outperforms many features of current state-of-the-art systems. Clinical Relevance-This work establishes that BioWolf16 is a wearable ultra-low power device enabling advanced multi-channel streaming and processing of medical-grade EMG signal, that can expand research opportunities and applications in healthcare and industrial scenarios

Smart Wearable Wristband for EMG based Gesture Recognition Powered by Solar Energy Harvester

With the recent improvement of flexible electronics, wearable systems are becoming more and more unobtrusive and comfortable, pervading fitness and health-care applications. Wearable devices allow non-invasive monitoring of vital signs and physiological parameters, enabling advanced Human Machine Interaction (HMI) as well. On the other hand, battery lifetime remains a challenge especially when they are equipped with bio-medical sensors and not used as simple data logger. In this paper, we present a flexible wristband for EMG gesture recognition, designed on a flexible Printed Circuit Board (PCB) strip and powered by a small form-factor flexible solar energy panel. The proposed wristband executes a Support Vector Machine (SVM) algorithm reaching 94.02 % accuracy in recognition of 5 hand gestures. The system targets healthcare and HMI applications, and can be used to monitor patients during rehabilitation from stroke and neural traumas as well as to enable a simple gesture control interface (e.g. for smart-watches). Experimental results show the accuracy achieved by the algorithm and the lifetime of the device. By virtue of the low power consumption of the proposed solution and the on-board processing that limits the radio activity, the wristband achieves more than 500 hours with a single 200 mAh battery, and perpetual work with a small-form factor flexible solar panel

Towards a novel HMI paradigm based on mixed EEG and indoor localization platforms

Location Based Services (LBS) have been gaining a great deal of attention thanks to their capability to enhance mobile services with location awareness. While outdoor localization is almost universally achieved via Global Positioning System (GPS), indoor localization is still challenging and a general solution is yet to be found. In a vision where wearable devices are taking over smartphones' leading role as gateway to the cyber world, new paradigms of interactive Human Machine Interfaces (iHMI) are arising. Among others, one of the most intriguing alternative iHMI is based on decoding the brain signals. Combining EEG activity data and indoor localization could dramatically improve the pervasiveness of the interaction between human, devices and environment. For these reasons, we propose a portable Hardware-Software platform that acquires brain EEG signals using a dedicated board along with position information from a cloud service. The positive results of the preliminary analysis successfully show the correlation between EEG signal and motion. Understanding that this is one of the first intents to merge these two sources of information, we intend to share publicly the ever-growing dataset to allow other researchers to investigate better the interaction between subjects and environments, and to lay the foundation of new paradigms in HMI

A wearable EEG-based drowsiness detection system with blink duration and alpha waves analysis

Drowsiness is one of the most prevalent causes of accidents in mining, driving and industrial activities carrying high personal risks and economic costs. For this reason, automatic detection of drowsiness is becoming an important application, and it is being integrated in a large variety of wearable and deeply embedded systems. Relevant effort has been spent in the past to quantify the drowsiness level from behavioral features exploiting eye tracking systems, dermal sensors or steering wheel movements. On the other hand, all these approaches lack of generality, they are highly intrusive and can only be applied in specific circumstances. A promising alternative approach is based on the extraction and processing of physiological features from the EEG using Brain Computer Interfaces (BCI). This work describes a wearable system capable of detecting drowsiness conditions and emitting alarms using only EEG signals, with three levels of alarm based on the blink duration and on the spectral power of alpha waves. This implementation aims to replace or complement the use of cameras and other sensors, extracting drowsiness information exploiting both behavioral and physiological features from EEG sensors only. The system was validated with 7 test subjects achieving detection accuracy of 85%, while being much more lightweight and compact than other state of the art methods

Towards versatile fast training for wearable interfaces in prosthetics

Developing embedded systems tailored for resource-constrained platforms enables the design of robust frameworks for controlling artificial arms in prosthetic applications. This work presents preliminary results of the implementation of a novel platform for EMG-based gesture recognition application based on Hyper dimensional Computing (HDC), a novel brain-inspired classifier. HDC reaches classification accuracy comparable with traditional statistical learning algorithms, while its training phase is one order of magnitude faster, resulting suitable for the implementation on low-power and low-cost digital platforms. The proposed setup acquires EMG data from 8 sensors, performs training in 1.2 s on the embedded microcontroller and classifies 5 gestures with 88% accuracy, a latency of 10ms and energy consumption of just 0.65 mJ per classification

A Wearable Device for Minimally-Invasive Behind-the-Ear EEG and Evoked Potentials

We present an unobtrusive and minimally invasive system for acquiring 8-channels of EEG behind the ear, to support brain activity monitoring in a socially acceptable fashion and outside clinical environments. Electrical performance are in line with those of clinical EEG systems (0.48 \u3bcV RMS integrated noise in the [0-100] Hz band, 106 dB CMRR) and setup is extremely fast, requiring only a mild cleaning of the skin before applying the device. The system can acquire typical brain activity rhythms and potentials evoked by sensory stimuli, which are at the basis of BCI systems. Current consumption is 6.2 rnA, plus 16 mA for Bluetooth\uae data transmission (4.5 hours life on a 100 mAh battery). The overall cost of components is below 50 USD

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

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