Design challenges for wearable EMG applications

Milosevic, Bojan; Benatti, Simone; Farella, Elisabetta

doi:10.23919/date.2017.7927217

Cited by 41 publications

(34 citation statements)

References 22 publications

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“…Several studies have proven recognition capabilities in controlled environments, but effective real-world adoption of EMG-based HMIs is still limited due to reliability 1 E3DA, ICT Center, FBK, Trento, Italy; email: {milosevic, efarella}@fbk.eu 2 Micrel Lab, Unversity of Bologna, Italy; email: simone.benatti@unibo.it problems [11], [13]. Research efforts now focus on more complex evaluations including postural effects and long term reliability [14], however the efforts of the research community are still fragmented and suffer from the lack of common benchmarks and datasets suitable for the analysis of variability.…”

Section: Introductionmentioning

confidence: 99%

Exploring Arm Posture and Temporal Variability in Myoelectric Hand Gesture Recognition

Milosevic¹,

Farella²,

Benaui

2018

2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

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Hand gesture recognition based on myoelectric (EMG) signals is an innovative approach for the development of intuitive interaction devices, ranging from poliarticulated prosthetic hands to intuitive robot and mobile interfaces. Their study and development in controlled environments provides promising results, but effective real-world adoption is still limited due to reliability problems, such as motion artifacts and arm posture, temporal variability and issues caused by the re-positioning of sensors at each use. In this work, we present an EMG dataset collected with the aim to explore postural and temporal variability in the recognition of arm gestures. Its collection of gestures executed in 4 arm postures over 8 days allows to evaluate the impact of such variability on classification performance. We implemented and tested Stateof-the-Art (SoA) recognition approaches analyzing the impact of different training strategies. Moreover, we compared the computational and memory requirements of the considered algorithms, providing an additional evaluation criteria useful for real-time implementation. Results show a decrease in the recognition of inter-posture and inter-day gestures up to 20%. The provided dataset will allow further exploration of such effects and the development of effective training and recognition strategies.

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Section: Introductionmentioning

confidence: 99%

Exploring Arm Posture and Temporal Variability in Myoelectric Hand Gesture Recognition

Milosevic¹,

Farella²,

Benaui

2018

2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

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“…For the past few decades, surface EMG, or just EMG, has been successfully used in medical and research applications that allow for the diagnosis of a wide range of motor and neural conditions [ 20 ]. EMG is a technique that measures the response of a muscle when an electrical stimulation is applied by the nerves [ 5 ].…”

Section: Background and Factors To Considermentioning

confidence: 99%

“…In clinical applications, an EMG is captured using dedicated medical instruments that are composed of silver-chloride (Ag/Cl) electrodes in combination with a conductive gel. The gel reduces the contact impedance with the skin, and this system is able to capture extremely high-quality signals that allow for the diagnosis of muscular and neural human systems [ 20 ].…”

Section: Background and Factors To Considermentioning

confidence: 99%

Wearable Hardware Design for the Internet of Medical Things (IoMT)

Qureshi

Krishnan

2018

Sensors

116

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As the life expectancy of individuals increases with recent advancements in medicine and quality of living, it is important to monitor the health of patients and healthy individuals on a daily basis. This is not possible with the current health care system in North America, and thus there is a need for wireless devices that can be used from home. These devices are called biomedical wearables, and they have become popular in the last decade. There are several reasons for that, but the main ones are: expensive health care, longer wait times, and an increase in public awareness about improving quality of life. With this, it is vital for anyone working on wearables to have an overall understanding of how they function, how they were designed, their significance, and what factors were considered when the hardware was designed. Therefore, this study attempts to investigate the hardware components that are required to design wearable devices that are used in the emerging context of the Internet of Medical Things (IoMT). This means that they can be used, to an extent, for disease monitoring through biosignal capture. In particular, this review study covers the basic components that are required for the front-end of any biomedical wearable, and the limitations that these wearable devices have. Furthermore, there is a discussion of the opportunities that they create, and the direction that the wearable industry is heading in.

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“…Such systems require accurate sensory interfaces and high computational capabilities to be implemented on systems with a reduced form factor, due to the intrinsically noisy nature of the EMG signal and on the computationally demanding algorithms required to make sense of the biosignals [9]. Some attempts have been made at a commercial level, such as the MYO [10], an armband that acquires EMG data from 8 differential channels and sends the data collected on EMG to a PC that processes them with pattern recognition techniques, to recognize up to 5 gestures.…”

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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Design challenges for wearable EMG applications

Cited by 41 publications

References 22 publications

Exploring Arm Posture and Temporal Variability in Myoelectric Hand Gesture Recognition

Exploring Arm Posture and Temporal Variability in Myoelectric Hand Gesture Recognition

Wearable Hardware Design for the Internet of Medical Things (IoMT)

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

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