Online Monitoring of Muscle Activity During Walking for Bio-feedback and for Observing the Effects of Transcutaneous Electrical Stimulation

Bunt, Nathan D.; Moreno, Juan C.; Müller, Philipp L.; Seel, Thomas; Schauer, Thomas

doi:10.1007/978-3-319-46669-9_116

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…The sensory system that we choose is Myo [21], which can be worn at the driver's forearm and integrates with an inertial measurement unit (IMU) [22][23][24] and eight EMG sensors [25][26][27]. The IMU chip contains an onboard digital motion processor (DMP) and MPU-9150 module which consists of a 3-axis accelerometer, a 3-axis gyroscope, and a 3-axis magnetometer.…”

Section: Wearable Sensory Systemmentioning

confidence: 99%

Hands-Free Maneuvers of Robotic Vehicles via Human Intentions Understanding Using Wearable Sensing

Wang

Guo

et al. 2018

Journal of Robotics

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Intelligent robotic vehicles are more and more fully automated, without steering wheels, gas/brake pedals, or gearshifts. However, allowing the human driver to step in and maneuver the robotic vehicle under specific driving requirements is a necessary issue that should be considered. To this end, we propose a wearable-sensing-based hands-free maneuver intention understanding approach to assist the human to naturally operate the robotic vehicle without physical contact. The human intentions are interpreted and modeled based on the fuzzy control using the forearm postures and muscle activities information detected by a wearable sensory system, which incorporates electromyography (EMG) sensors and inertial measurement unit (IMU). Based on the maneuver intention understanding model, the human can flexibly, intuitively, and conveniently control diverse vehicle maneuvers only using his intention expressions. This approach was implemented by a series of experiments in the practical situations on a lab-based 1/10 robotic vehicle research platform. Experimental results and evaluations demonstrated that, by taking advantage of the nonphysical contact and natural handleability of this approach, the robotic vehicle was successfully and effectively maneuvered to finish the driving tasks with considerable accuracy and robustness in human-robotic vehicle interaction.

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Section: Wearable Sensory Systemmentioning

confidence: 99%

Hands-Free Maneuvers of Robotic Vehicles via Human Intentions Understanding Using Wearable Sensing

Wang

Guo

et al. 2018

Journal of Robotics

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Bipedal gait model for precise gait recognition and optimal triggering in foot drop stimulator: a proof of concept

Shaikh

Salčić

Wang

et al. 2018

Med Biol Eng Comput

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Electrical stimulators are often prescribed to correct foot drop walking. However, commercial foot drop stimulators trigger inappropriately under certain non-gait scenarios. Past researches addressed this limitation by defining stimulation control based on automaton of a gait cycle executed by foot drop of affected limb/foot only. Since gait is a collaborative activity of both feet, this research highlights the role of normal foot for robust gait detection and stimulation triggering. A novel bipedal gait model is proposed where gait cycle is realized as an automaton based on concurrent gait sub-phases (states) from each foot. The input for state transition is fused information from feet-worn pressure and inertial sensors. Thereafter, a bipedal gait model-based stimulation control algorithm is developed. As a feasibility study, bipedal gait model and stimulation control are evaluated in real-time simulation manner on normal and simulated foot drop gait measurements from 16 able-bodied participants with three speed variations, under inappropriate triggering scenarios and with foot drop rehabilitation exercises. Also, the stimulation control employed in commercial foot drop stimulators and single foot gait-based foot drop stimulators are compared alongside. Gait detection accuracy (98.9%) and precise triggering under all investigations prove bipedal gait model reliability. This infers that gait detection leveraging bipedal periodicity is a promising strategy to rectify prevalent stimulation triggering deficiencies in commercial foot drop stimulators. Graphical abstract Bipedal information-based gait recognition and stimulation triggering.

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Intuitive Maneuver of Autonomous Vehicles without Physical Control Interfaces using Wearable Sensing Devices

Wang

Diekel

et al. 2017

2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

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Online Monitoring of Muscle Activity During Walking for Bio-feedback and for Observing the Effects of Transcutaneous Electrical Stimulation

Cited by 4 publications

References 2 publications

Hands-Free Maneuvers of Robotic Vehicles via Human Intentions Understanding Using Wearable Sensing

Hands-Free Maneuvers of Robotic Vehicles via Human Intentions Understanding Using Wearable Sensing

Bipedal gait model for precise gait recognition and optimal triggering in foot drop stimulator: a proof of concept

Intuitive Maneuver of Autonomous Vehicles without Physical Control Interfaces using Wearable Sensing Devices

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