Human arm movement sensing using linear carbon potentiometers

IJSTE - International Journal of Science Technology & Engineering | Volume 2 | Issue 09 | March 2016 ISSN (online): 2349-784X

Human Arm Movement Data Acquisition and Processing Myron Velton Rodrigues UG Student Department of Electronics and Telecommunication Don Bosco College of Engineering

Abdul Rakib M. R. Sarmadi UG Student Department of Electronics and Telecommunication Don Bosco College of Engineering

Sunny Rudresh Sinari UG Student Department of Electronics and Telecommunication Don Bosco College of Engineering

Veliston Joao Rocha UG Student Department of Electronics and Telecommunication Don Bosco College of Engineering

Anisha Maria Cotta Assistant Professor Department of Electronics and Telecommunication Don Bosco College of Engineering

Abstract In recent times there is an increasing need for robots for operating in different inhumane situations. They may involve collecting data from harsh environments, military use, to carrying out maintenance work on space stations. The use of such robots requires skilled personnel as the controllers used are not human friendly. Here we propose to build a human arm interface which can read and process data to be further used by the controlling robots through the use of liner carbon potentiometers. For proper control and to reduce the noise coming from the potentiometers we use an averaging algorithm to smoothen out the output. This data can then be used to control various robots by further mapping the inputs and outputs. Finally, this interface is expected to reduce human efforts in controlling robots.

Keywords: Linear carbon potentiometers, Averaging algorithm, Arduino ________________________________________________________________________________________________________ I.

INTRODUCTION

Nowadays, robots are increasingly being integrated in working tasks to replace humans. Robots are increasingly being used in different fields of operation including production lines in industries, military, hospitals and space exploration. Some robots have a built in feedback systems which enables them to self-control their movements while others use an open loop system where human control is required [1-2]. It becomes a tedious job to train personnel for controlling these robots and many a times the operating conditions are harsh for humans. Therefore a simple human arm interface which records natural human arm movements and digitizes this data can prove handy for controlling robots.

Currently there are various sensors available to record human movements such as pressure sensors, flex sensors, biomedical sensors which are quite expensive hence we use simple linear carbon potentiometers placed at every joint on the human arm to record motion [3-4]. The inbuilt analog to digital convertor of the Arduino UNO development board which uses Atmega328 Microcontroller is used to digitize the analog data from potentiometers. The potentiometers can prove noisy hence the Moving averages algorithm is used to smoothen out the data [5]. This data can then be either stored for programming robots in industries to perform a particular task or it can be live streamed to the robot for real time operation.

II. BLOCK DIAGRAM

Fig. 1: Block Diagram representation of the proposed human arm interface.

The block diagram in Fig.1. Shows that the linear carbon potentiometers are connected to the microcontroller which converts the analog input data to digital data via the inbuilt analog to digital converter and then this output is processed by the microcontroller processing unit to remove any noise. The output from the processing unit is finally stored to be used by robots or is live streamed to a robot to control it in real time.

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Human Arm Movement Data Acquisition and Processing (IJSTE/ Volume 2 / Issue 09 / 052)

III. DESIGN AND DESCRIPTION Hardware Design As shown in Fig. 2. The hardware required is linear carbon potentiometers to be placed at the joints of fingers (index and thumb), Wrist, Elbow and Shoulder. The potentiometers are then connected to the microcontroller.

Software Design The software part involves acquiring the data from the potentiometers via the Analog to Digital converter and processing it to smoothen out the output using the Moving averages algorithm this reduces the noise that might result in sudden spikes in the output data.

Fig. 2: Circuit Diagram showing sensor connections to the Arduino UNO

IV. IMPLIMENTATION Hardware implementation Fig. 3. Shows the interface connected to a human arm. The static terminals of the potentiometers are shorted and connected to Supply of 5 volts and common ground. The slider terminal of each potentiometer is connected to a separate Analog to Digital converter channel on the Arduino UNO. Acrylic material is used to make the moving and static shaft connected to the knob and body respectively of the potentiometer. The acrylic shafts are fastened to human arm with Velcro as shown in Fig. 3. Human arm movements cause the acrylic shafts to move and in turn rotate the potentiometer slider which in turn changes the voltage output at the slider terminal.

The Arduino UNO board has a 10 bit Analog to Digital convertor hence the values for full swing voltage range from 0 (potentiometer slider to minimum position) to 1023 (potentiometer slider to maximum position).

Fig. 3: The Designed interface connected to human arm.

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Human Arm Movement Data Acquisition and Processing (IJSTE/ Volume 2 / Issue 09 / 052)

Software implementation The code to acquire data from the potentiometers is developed using the Arduino IDE which uses C++ language. The Smoothing algorithm of Moving averages is implemented on Arduino itself. The first element of the moving average is obtained by averaging a fixed number of initial elements in the given set of readings. The subsequent averages are obtained by eliminating the first element and including the next element after the last element of previous subset. This process is repeated over the entire data series. The real time plot of the original reading is obtained using an inbuilt graph plotter in the Arduino IDE software and is shown in Fig. 4. Also the smoothened output is shown in Fig. 5. and Fig. 6. It is seen that the output signal is smoothened more as the window size of the moving average algorithm is increased.

Fig. 4: Graph of the original reading taken from one of the sensors

Fig. 5: Graph showing output of the moving average algorithm with window size 5

Fig. 6: Graph showing output of moving average algorithm with window size 10

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Human Arm Movement Data Acquisition and Processing (IJSTE/ Volume 2 / Issue 09 / 052)

V. CONCLUSION The human arm interface can be used to program industrial robots and various other robots which use robotic arms for their operation. It significantly reduces the workload of programming robots and controlling them with joystick controllers. The processing done on the input movement data helps in reducing noise spikes ensuring smooth movement of interfaced robotic arms.

REFERENCES [1]

[2] [3] [4] [5]

Md. Bony Amin, G.M. Sultan Mahmud Rana, Abdullah-Al-Farabi, A.M.M. Nazmul Ahsan, Md. Ahasan Habib; “Design and Implementation of a Remote Controlled Robotic Arm Based on Industrial Application Perspective,” International Conference on Mechanical, Industrial and Materials Engineering 2013, RUET, Rajshahi, Bangladesh B.O.Omijeh, R.Uhunmwangho, M.Ehikhamenle; “Design Analysis of a Remote Controlled “Pick and Place” Robotic Vehicle,” International Journal of Engineering Research and Development, Volume 10, Issue 5 (May 2014), PP.57-68 Vikas R Thakur, Snehal M Gajbhiye; “Wireless Monitoring of Human Limb Motion,” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol.5 (3), 2014, 4222-4225 K. D. Nguyen, I. M. Chen ; Z. Luo ; S. H. Yeo ; H. B. L. Duh; “A Wearable Sensing System for Tracking and Monitoring of Functional Arm Movement,” IEEE/ASME Transactions on Mechatronics (Volume:16 , Issue: 2) José Luis Guiñón, Emma Ortega, José García-Antón, Valentín Pérez-Herranz; “Moving Average and Savitzki - Golay Smoothing Filters Using Mathcad,” International Conference on Engineering Education – ICEE 2007, Coimbra, Portugal.

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