EMG for controlling RoboTrainer Light - Revision history

Andb: /* Project description */

2020-02-11T13:51:57Z

Project description

Jemat17 at 13:36, 10 February 2020

2020-02-10T13:36:38Z

Jemat17 at 13:36, 10 February 2020

2020-02-10T13:36:29Z

Jemat17 at 13:36, 10 February 2020

2020-02-10T13:36:15Z

Jemat17 at 13:34, 10 February 2020

2020-02-10T13:34:12Z

Ralyn17: Created page with "The aim of this project is to investigate whether surface EMG signals, can be used to dynamically control a training robot, the RoboTrainer Light. The EMG sensors records the..."

2020-02-10T13:32:48Z

Created page with "The aim of this project is to investigate whether surface EMG signals, can be used to dynamically control a training robot, the RoboTrainer Light. The EMG sensors records the..."

New page

The aim of this project is to investigate whether surface EMG signals, can be used to dynamically control a training robot, the RoboTrainer Light. The EMG sensors records the electrical activity produced by nerve system to activate the skeletal muscles.
The study will consist of three different phases. The first phase consists of exploring the different opportunities regarding the use of EMG for controlling the training robot. Further select the most promising setups.
The second phase consists of writing a program for the setups to be able to conduct the training sessions on the selected setups, and further conduct the training sessions. The exercise could for example be a biceps curl, where EMG electrodes would be placed on the biceps.
The third phase consists of evaluating the outcome and document the project in the thesis. The study will have an engineering approach, with few test subjects.
The RoboTrainer Light has been chosen due to its ability to control and measure different parameters and control the parameters in training setups. Hereby see if the biofeedback will cause any change in the outcome of the exercise.

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 Jeppe Dreyer Matzen
-jemat17@student.sdu.dk
+jemat17 (at) student (dot) sdu (dot) dk
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 Rasmus Lynggaard
-ralyn17@student.sdu.dk
+ralyn17 (at) student (dot) sdu (dot) dk

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 jemat17@student.sdu.dk
 Rasmus Lynggaard
 ralyn17@student.sdu.dk

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 Jeppe Dreyer Matzen
 jemat17@student.sdu.dk
 Rasmus Lynggaard
 ralyn17@student.sdu.dk

← Older revision		Revision as of 13:36, 10 February 2020
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	The third phase consists of evaluating the outcome and document the project in the thesis. The study will have an engineering approach, with few test subjects.		The third phase consists of evaluating the outcome and document the project in the thesis. The study will have an engineering approach, with few test subjects.
	The RoboTrainer Light has been chosen due to its ability to control and measure different parameters and control the parameters in training setups. Hereby see if the biofeedback will cause any change in the outcome of the exercise.		The RoboTrainer Light has been chosen due to its ability to control and measure different parameters and control the parameters in training setups. Hereby see if the biofeedback will cause any change in the outcome of the exercise.
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		+	Jeppe Dreyer Matzen
		+	jemat17@student.sdu.dk
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		+	Rasmus Lynggaard
		+	ralyn17@student.sdu.dk

← Older revision		Revision as of 13:34, 10 February 2020
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		+	== Project description ==
	The aim of this project is to investigate whether surface EMG signals, can be used to dynamically control a training robot, the RoboTrainer Light. The EMG sensors records the electrical activity produced by nerve system to activate the skeletal muscles.		The aim of this project is to investigate whether surface EMG signals, can be used to dynamically control a training robot, the RoboTrainer Light. The EMG sensors records the electrical activity produced by nerve system to activate the skeletal muscles.
	The study will consist of three different phases. The first phase consists of exploring the different opportunities regarding the use of EMG for controlling the training robot. Further select the most promising setups.		The study will consist of three different phases. The first phase consists of exploring the different opportunities regarding the use of EMG for controlling the training robot. Further select the most promising setups.