Flex Your Muscles: EMG-Based Serious Game Controls

Müller, Philipp Niklas; Achenbach, Philipp; Kleebe, André Mihca; Schmitt, Jan Ulrich; Lehmann, Ute; Tregel, Thomas; Göbel, Stefan

doi:10.1007/978-3-030-61814-8_18

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The paper by [Müller et al 2020] studied the viability of the MyoWare sensor as an input method for serious games, concluding that it can achieve satisfactory results, given that the game does not require fast reflexes or precise movement, which aligns with the goals of the present work. Another article [Follmann 2019] makes use of the same sensor, applied in a serious game, to be used as part of the rehabilitation from upper extremity amputation.…”

Section: Related Worksupporting

confidence: 55%

EMG Based Serious Game For Use in Stroke Rehabilitation

Silva,

Guerra

2023

Anais Estendidos Do XXII Simpósio Brasileiro De Jogos E Entretenimento Digital (SBGames Estendido 2023)

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This paper introduces a proof-of-concept for a serious game that uses electromyography (EMG) signals as input, with the aim of improving rehabilitation treatment for patients experiencing stroke sequelae. The prototype, developed with the Unity game engine, incorporates a low-cost hardware device for reading and processing EMG signals. Communication between software and sensor is enabled through Bluetooth using an ESP32 microcontroller. The prototype includes a secondary monitor with a virtual hand to emulate Mirror Therapy. The positive evaluation of a physiotherapist validates the project for future development.

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Section: Related Worksupporting

confidence: 55%

EMG Based Serious Game For Use in Stroke Rehabilitation

Silva,

Guerra

2023

Anais Estendidos Do XXII Simpósio Brasileiro De Jogos E Entretenimento Digital (SBGames Estendido 2023)

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“…The ML algorithm used was Ensemble due to its high accuracy (93.9%) and prediction time, as shown in Table 1. The generalization of the trained model was satisfied with all subjects reviewed without the need for recalibration or retraining, which improved usability and ease of setup (in plug-and-play settings) as recommended in playing the game using bio-signals [41,[48][49][50].…”

Section: Discussionmentioning

confidence: 85%

“…Alternative controllers focus on new input signals for analysis. These include, but are not limited to, voice commands, mouth, vision-based gestures [36], and bio-signals [37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments

Muguro

Laksono

Rahmaniar

et al. 2021

Signals

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In recent years, surface Electromyography (sEMG) signals have been effectively applied in various fields such as control interfaces, prosthetics, and rehabilitation. We propose a neck rotation estimation from EMG and apply the signal estimate as a game control interface that can be used by people with disabilities or patients with functional impairment of the upper limb. This paper utilizes an equation estimation and a machine learning model to translate the signals into corresponding neck rotations. For testing, we designed two custom-made game scenes, a dynamic 1D object interception and a 2D maze scenery, in Unity 3D to be controlled by sEMG signal in real-time. Twenty-two (22) test subjects (mean age 27.95, std 13.24) participated in the experiment to verify the usability of the interface. From object interception, subjects reported stable control inferred from intercepted objects more than 73% accurately. In a 2D maze, a comparison of male and female subjects reported a completion time of 98.84 s. ± 50.2 and 112.75 s. ± 44.2, respectively, without a significant difference in the mean of the one-way ANOVA (p = 0.519). The results confirmed the usefulness of neck sEMG of sternocleidomastoid (SCM) as a control interface with little or no calibration required. Control models using equations indicate intuitive direction and speed control, while machine learning schemes offer a more stable directional control. Control interfaces can be applied in several areas that involve neck activities, e.g., robot control and rehabilitation, as well as game interfaces, to enable entertainment for people with disabilities.

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“…Furthermore, by exploiting the other actor of the HMI (i.e., the machine), hand gesture recognition can be involved also in the (tele-)rehabilitation field, both with passive and active approaches. Indeed, standard rehabilitative sessions can motivate the patients making them interact with the so-called serious games [23]- [25] or by giving them more simple feedback during movements execution [26]- [28]. On the other hand, for people which are not able to autonomously perform the rehabilitation routine, the therapist can, for example, apply the functional electrical stimulation on the injured muscle(s), both involving data compression [29] and maintaining the association of the gestures one by one [30], [31].…”

Section: Introductionmentioning

confidence: 99%

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

Mongardi

Rossi

Prestia

et al. 2022

IEEE Trans. Biomed. Circuits Syst.

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Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface Elec-troMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9 % when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92 mA of current absorption during active functioning and 1.34 ms prediction latency, this prototype confirmed our expectations and can be an appealing solution for longterm (up to 60 h) medical and consumer applications.

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Flex Your Muscles: EMG-Based Serious Game Controls

Cited by 5 publications

References 12 publications

EMG Based Serious Game For Use in Stroke Rehabilitation

EMG Based Serious Game For Use in Stroke Rehabilitation

Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

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