An Upper Limb Paralysis Rehabilitation Activity Monitoring Device for Post-Traumatic Stroke Patients

Download Full-Text PDF Cite this Publication

Text Only Version

An Upper Limb Paralysis Rehabilitation Activity Monitoring Device for Post-Traumatic Stroke Patients

Preethika Immaculate Britto1

1Assistant Professor, Biomedical Engineering Division,

School of Biosciences and Technology, VIT University, Vellore

Vidhya.S2 Assistant Professor Senior,

Biomedical Engineering Division, School of Biosciences and Technology, VIT University, Vellore,

Kalluri Yogeshwar Reddy3*

3B. Tech,

School of Electronics and Communication Engineering, VIT University, Vellore,

Abstract-This paper provides a comparative analysis of rehabilitation activity after stroke. Monitoring rehabilitation activity for post-stroke patients has been increasingly common in hospitals and rehabilitation centers worldwide. To develop a home based and low cost device for post-stroke patients activity monitoring is challenging in rehabilitation engineering. Many technological devices have been developed (assistive device, exoskeleton device, and robotic device) to recover daily living activities particularly for stroke patients. These are complex in use for home based training and are also cost ineffective. We identify several functional activities of finger and wrist joint which allow investigating the patterns of finger and wrist functional activity for both normal and upper limb paralyzed subjects. These studies have helped to investigate the activation patterns in different experimental conditions such as water bottle take off, wrist stretching and grasp force. Furthermore, comparative studies have been performed in order to provide detailed functional activity for upper limb paralyzed subjects.

Keywords: Data Glove, Rehabilitation activity, Wireless sensor system


    Measuring upper limb mobility and coordination in subjects suffering from paralyzed conditions is very difficult because of their obvious immobility. It is important for post-stroke patients to regain the mobility and fitness. Rehabilitation programs help such patients to reduce the complexity of treatment carried out by well- trained people like doctors and physiotherapists. They advised and motivated the patients to do the exercises and undergo control of their movements such that they can regain their normal routine.

    Thus rehabilitation process plays a major role in regaining the loss of motor function. Rehabilitation process involves extensive physical exercises that may help to regain the functional disabilities. There are lots of devices such as assistive devices, robotics devices, exoskeleton devices that are used to carry out upper limb paralysis rehabilitation.

    These systems are extremely costly and complex while also not being suitable for home based rehabilitation training. There are some therapeutic approaches that can be used to regain the loss of activity of daily living (ADL) for paralysis patients for example Constraint induced movement therapy (CIMT), which is a recent advancement in stroke rehabilitation. In this approach patients are advised to constraint their unaffected limb and train the affected limb to do the activity of daily living.

    In this proposed system designed with a wireless wearable sensor network, multiple sensors (accelerometer, flex sensor, force sensitive resistor) are used for tracking the range of motion and muscle force of the upper limb extremities. These sensors pick up the signals which are associated with upper limb range of motion. Later this analog signal is converted into digital signal using an 8- channel multiplexing analog to digital converter. Further, this digital data is sent to the microcontroller unit for processing the data which is further sent to the personal computer through wireless Zig Bee protocol. Finally, these signals are analyzed then compared with normal subjects to know the improvement rate of rehabilitation activity. The results can be kept for further analysis.

  2. Hardware Components

    1. System Setup

      Fig. 1 shows the overall system block diagram. It consists of two units: First unit is related to data acquisition and the Second unit consists of the master controller part. Data acquisition part consists of the multiple sensor system (Accelerometer, Flex sensor, Force sensitive resistor sensor) and an analog to digital converter. In the second unit Digital data has been processed by AT89c51 Microcontroller and this processed data is transmitted through the Zig Bee wireless protocol. In the receiver section this data is received by another Zig Bee wireless protocol and then monitored on a personal computer for data analyzing.

      Fig. 1: Overall system block diagram

    2. Flex sensor

      Flex sensor, also called bend sensor, is used to detect the bending action. Inside the flex sensor are carbon resistive elements within a thin flexible substrate. More carbon means less resistance. When the substrate is bent, the sensor produces a resistance output relative to the bend radius. This converts the change in bend to electrical resistance – the more the bend, the more the resistance value. Initially bend sensors being straight, the resistance value is about 10k ohms. When its bend about 90 degree, the resistance increases to 30-40K ohms. Here the flex sensor has been used to detect bending movement of finger and particularly, while making a fist and to monitor object grasp finger movements. Flex sensors are used in gaming gloves, auto controls, fitness products, measuring devices, assistive technology, musical instruments, joysticks, and more. Life cycle of bend sensor is more than 1 million times for a Height of 73.66mm and temperature range between -35°C to +80°C.

    3. Force sensitive resistor (FSR) sensor

      The Force sensitive resistor sensor is used to detect the applied force. This sensor is composed of a thick polymer film. When its active surface is touched, résistance value can be obtained. The FSR varies its resistance depending on how much pressure is applied to the sensing area. Resistance value of FSR is inversely proportional to the applied force on active surface; and hence, the greater the force, the lower the resistance. The sensor has a resistance of greater than 1M when no pressure is applied. The force sensitive resistor sensor has been used for measuring the force response during making of a fist.

    4. Accelerometer

      An Accelerometer is used to detect the static and dynamic motions. Here ADXL335- a thin, small, low power 3- axis motion tracking accelerometer has been used. It measures acceleration indirectly through an applied force. It contains a poly-silicon surface micro machined structure. It has a differential capacitor which consists of two fixed plates attached to the moving mass this setup is used to measure the deflection of structure. The acceleration deflects the moving mass and unbalances the differential capacitor resulting in a sensor output whose amplitude is proportional to acceleration. Here, this accelerometer has been used to detect the motion of wrist joint flexion and extension.

    5. Analog to Digital Converter

      An Analog to digital converter is a data acquisition component used to convert analog signals into digital data. We have used ADC0809 8-bit analog to digital converter, 8-channel multiplexer and microprocessor compatible control logic. The 8-bit A/D converter uses successive approximation conversion technique so that the sensors analog voltage output can be converted into digital data.

    6. Microcontroller

      The AT89C51 microcontroller has been used which is low power consumption, high-performance CMOS 8-bit microcontroller. It has K Bytes of In-System Reprogrammable Flash Memory, 128 bytes of RAM, 32 I/O lines, two 16-bit timer/counters, five vector two-level interrupt architecture, a full duplex serial port, on-chip oscillator and clock circuitry. The entire system has been controlled by this master controller.

    7. Zig Bee

      Here, CC2500 Zig Bee module has been preferred which is a Single Chip, Low Cost, Low Power RF Transceiver and has an operating frequency range between 2400 2483.5 MHz. It uses FSK modulation technique for data transmission. It gives a 30 meters range with onboard antenna. It can be used on wireless security system or specific remote-control function and others wireless systems. This module supports various modulation formats and has a configurable data rate up to 500 K baud.

    8. Software

      Keil uVision 3 Integrated Development Environment (IDE) is a program platform used to develop program for 8051microcontroller board. The coding language is embedded C. Also MATLAB has been used to obtain the graphical representation of data.


    1. Device development

      Fig. 2(a) shows the transmitter section of the device for monitoring upper limb rehabilitation activity. In this proposed device, a 9V battery for power supply has been chosen. This device can be attached on subject hand above wrist joint with the help of soft material bought from an Orthotics & Prosthetics center. This material also gives comfortable feel to subject and the wire disturbances and electrical shocks can be isolated.

      Fig. 2(a): Transmitter section of device

      Fig. 2(b) shows the receiver section of the device. The processed data is transmitted by The Zigbee module of transmitter section and received by Zigbee in receiver section depicted in Fig. 2(b). This data is converted from the TTL voltage level to RS232 voltage level by MAX232. This data can now be monitored in a PC through RS232 serial port. This proposed device has been designed to be a low cost, light weight and highly secured system that does not restrict the patients movements.

      Fig. 2(b): Receiver section of device

    2. Sensing system

      In the development of an upper limb paralysis rehabilitation activity monitoring device in which a multiple sensor system (force sensitive resistor sensor, Accelerometer, flex sensor) has been proposed for tracking the finger and wrist joint movements. So a cotton working glove in Fig. 3has been chosen which is widely available in market for mounting the sensors on it and it was specially modified for placing the sensors. Fig. 3 shows the placement of the Flex sensors, Accelerometer and FSR sensor. These sensors are connected to the transmitter sections analog input pin through wires for analog signal detection. Then, the analog signal is converted to digital data by the analog to digital converter. This digital data will be sent to the AT89C51 microcontroller.

      Fig. 3: Modified cotton data glove

    3. Data monitoring

      Here, the Terminal serial port software has been used for monitoring data. Fig. 4 shows the terminal software snap shot. Whenever a finger or the wrist is bent, the sensors will detect the movement and the microcontroller will transmit data to the PC in which Terminal will display the data output. The output of analog signal from Accelerometer, flex sensor and FSR sensor are logged to PC by Terminal software where this received data is saved in a .txt format. The .txt files can be further processed into MATLAB to create graphical representation. This graphical representation clearly shows the signal response of movements and further comparative analysis can be done.

      Fig. 4: Snap shot of Terminal software

    4. Experimentation

    Three distinctive movements have been proposed: Finger fist making, wrist flexion and extension and water bottle take off. Data has been acquired for 5 normal subjects and 2 paralyzed patients shown in Fig. 5. Flex sensors are used to detect the fist-making movement activity. FSR is used to detect the applied force signal response while making finger fist. Accelerometer is used to track the motion of water bottle take off and wrist flexion & extension. In order to validate the results, arepeated experimentation has been performed and analysis was be carried out according to characteristics of movements.

    Fig 5: Summary of Subject details


    In this paper, a wearable sensor accelerometer, flex sensor and FSR sensor have been used for the development of monitoring the rehabilitation activity. The wireless data transmission has been achieved using Zig Bee protocol. In order to examine the performance of the proposed system, an experiment has been conducted for both subjects (Normal, Patient). Several typical rehabilitation experiments, such as wrist flexion & extension, finger fist making movement, force during fist making and water bottle takes off have been performed. In this project, rehabilitation activity for both normal and paralyzed subjects has been monitored and comparative analysis has been performed. During rehabilitation activity, the data was been recorded in real time. This proposed method allows the monitoring of subjects activity without restraint and rehabilitation can be carried out in home environment instead of specialized laboratory in hospital and also without supervision of physiotherapist.


  1. Abdul Malik Bin Mohd Ali, Muhammad Shukri Bin Ahmad, Muhammad Mahadi Bin Abdul,Jamil Radzi Bin Ambar, Hazwaj Bin Mhd Poad ( 2012). Multi-sensor Arm Rehabilitation MonitoringDevice, International Conference on Biomedical Engineering (ICoBE), Penang, Malaysia, 27-28 February 2012.

  2. Ju Wang, Jiting Li, Yuru Zhang, Shuang Wang (2009). Design of an Exoskeleton for Index Finger Rehabilitation, 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 2009.

  3. Sofia Olandersson, Helene Lundqvist, Martin Bengtsson, Magnus Lundahl, Albert-Jan Baerveldt and Marita Hilliges (2005). Finger- force measurement-device for hand rehabilitation, 9th International Conference on Rehabilitation Robotics June 28 – July 1, 2005, Chicago, IL, USA.

  4. Uros mali and marko munih (2006). Hife-haptic interface for finger exercise, IEEE transactions on mechatronics, vol. 11, no. 1, February 2006.

  5. Kexin Xing, Jian Huang, Qi Xu, Yongji Wang (2009). Design of A Wearable Rehabilitation Robotic Hand Actuated by Pneumatic Artificial Muscles, 7th Asian Control Conference, Hong Kong,

    China, August 27-29, 2009

  6. K S Low, G. X. Lee, T. Taher (2009). A Wearable Wireless Sensor Network for Human Limbs Monitoring, International Instrumentation and Measurement Technology Conference Singapore, 5-7 May 2009

  7. Chee Kian Lim, I-Ming Chen, Zhiqiang Luo, Song Huat Yeo (2010). A Low Cost Wearable Wireless Sensing System for Upper Limb Home Rehabilitation, IEEE conference on Robotics Automation and Mechatronics, 28-29 June 2010.

  8. Junho park, Inje Univ, Gimhae (2007). A Zigbee Network-based Multi-channel Heart Rate Monitoring System for Exercising Rehabilitation Patients, TENCON 2007.

  9. Muhammad Ali Mazidi, Janice Gillispie Mazidi, Rolin D. Mckinlay, (2006), The 8051 Microcontroller and embedded systems using Assembly and C, Prentice, Hall of India Private limited.

  10. L. K. Simone, E. Elovic, U. Kalambur, D. Kamper (2004). A Low Cost Method to Measure Finger Flexion in Individuals with Reduced Hand and Finger Range of Motion, Annual International Conference of the IEEE EMBS San Francisco, CA, USA September 1-5, 2004.

Leave a Reply

Your email address will not be published. Required fields are marked *