Exploring the Feasibility of EMG Based Interaction for Assessing Cognitive Capacity in Virtual Reality

Wirth, Markus; Grad, Stefan; Poimann, Dino; Richer, Robert; Ottmann, Jenny; Eskofier, Bjoern M.

doi:10.1109/embc.2018.8513213

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…For this reason, systems have been developed, such as the Rehabilitation gaming system (RGS) [11], Computer Assisted Rehabilitation Environment (CAREN) [12] or Virtual Assisted Rehabilitation System (VARS, lowcost solution) [13]. Most of the systems using high immersive technologies like VR has a positive effect on minimizing recovery time and training progress [14].…”

Section: Related Workmentioning

confidence: 99%

Gesture Interaction in the Lirkis Cave Based on Emg and Kinect Sensor

Pastornicky¹,

Chovanec²

2020

AEI

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The article deals with the possibilities of interaction in the virtual environment (VE) of the CAVE LIRKIS system. Commonly available peripherals are keyboard, mouse or gamepad. However, these do not provide a sufficient level of immersion, such as hand gestures. The MYO armband has proven to be one of the potential devices for improving interaction. Therefore, a system for editing an object transformation in VE was created using hand gestures and movements. Enhancing the natural user interface (NUI) of the system with additional interaction capabilities has proven the device to be suitable also for further development.

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Section: Related Workmentioning

confidence: 99%

Gesture Interaction in the Lirkis Cave Based on Emg and Kinect Sensor

Pastornicky¹,

Chovanec²

2020

AEI

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“…Generally, the dynamic sports data are in the form of various heterogeneous physiological signals [12], such as heart rate, oxygen consumption, carbon dioxide, oxygen concentration, electromyography (EMG), electroencephalogram (EEG), and blood lactate. These data are usually collected by using a set of external sensors and wearable devices, such as spirometers, ergometers [12], myo armband [7], electrode cap [11], and microwave sensors [1]. However, for collecting more details about the athlete's postures, decisions, and actions, visual…”

Section: Introductionmentioning

confidence: 99%

Object-Level Trajectories Based Fine-Grained Action Recognition in Visual IoT Applications

Xiong

Wang

et al. 2019

IEEE Access

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The emerging computer vision and deep learning technologies are being applied to the intelligent analysis of sports training videos. In this paper, a deep learning based fine-grained action recognition (FGAR) method is proposed to analyze soccer training videos. The proposed method was applied to indoor training equipment for evaluating whether a player has stopped a soccer ball successfully or not. First, the problem of FGAR is modeled as human-object (player-ball) interactions. The object-level trajectories are proposed as a new descriptor to identify fine-grained sports videos. The proposed descriptor can take advantage of high-level semantic and human-object interaction motions. Second, a cascaded scheme of deep networks based on the object-level trajectories is proposed to realize FGAR. The cascaded network is constructed by concatenating a detector network with a classifier network (a long-short-termmemory (LSTM)-based network). The cascaded scheme takes the advantage of the high efficiency of the detector on object detection and the outstanding performance of the LSTM-based network on processing time series. The experimental results show that the proposed method can achieve an accuracy of 93.24%.

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