Robotic System for Upper Limb Rehabilitation Mauricio Torres1, Roberto Sagaró2, Leonardo Broche2, Denis Delisle3, Angel Reyes3, Alberto López3, and Esteban Rossi4 1 Technical College Institute Juan Bosco, Bogotá, Colombia Faculty of Mechanic Engineering, University of Oriente, Santiago de Cuba, Cuba 3 Centre of Medical Biophysics, University of Oriente, Santiago de Cuba, Cuba 4 Faculty of Engineering, National University of Entre Ríos, Paraná, Argentina
[email protected], {sagaro2001,leobroche,delisle05}@gmail.com,
[email protected],
[email protected] 2
Abstract— Currently, cerebrovascular diseases are one of the main health problems. Part of the patient’s rehabilitation process, affected by this disease, is manually performed by a physiotherapist, which, due to physical exhaustion, could affect the performance of patient recovery. In this paper is proposed a robotic exoskeleton for upper limb rehabilitation, which enables assist or supports the therapist’s work. In the first stage, the exoskeleton is controlled passively through programmed commands and routines. Later, a second stage is proposed for biofeedback control system using the exoskeleton and signals acquired through bioinstrumentation equipment. This system will allows the acquisition of the surface electromyography signals (sEMG), as well as proprioceptive information for signal processing and movement’s intention detection of upper limb. As results, are presented the implementation of robotic arm commanded passively and the bioinstrumentation equipment is presented. In the rehabilitation field, this assistive technology will enable to medical staff, to contribute to recovery and welfare of the patient, affected by some kind of muscular dysfunction, with major effectiveness.
aforementioned ideas [6, 7]. The SUEFUL-7 exoskeleton uses sEMG, the force signals of the forearm and hand as additional information, and torque of the forearm. This integration allows the activation of exoskeleton even with small amplitude of the sEMG [6]. The NEUROExos uses an EMG-based control method which estimates the required torque for the patient to operate the exoskeleton. The experimental results show the achievement reduction of necessary effort for the subject to generate the upper limb movement [7]. The aims of this work is to design and build a rehabilitation system based in upper limb exoskeleton of four degree of freedom (DOF) and sEMG equipment, to assist movements of people affected by stroke, with passive and active control in first and second stage, respectively.
Keywords—robotic exoskeleton, bioinstrumentation system, muscular rehabilitation, surface electromyographic signals.
Figure 1 shows the general diagram of the proposed system, composed by two blocks: robotic exoskeleton and control system. The control system is divided in three blocks: bioinstrumentation equipment employed to provides active control of the exoskeleton; the actuator control board of the exoskeleton and the user interface. Next, will be presented the functional description of systems and subsystems that compose the robotic system for upper limb.
I. INTRODUCTION Currently, fifteen millions of people annually suffer Stroke in the world. Five millions of these people lost their life and other five millions remain with some degree of disability [1]. The statistics forecast that the cerebrovascular accidents will be the first cause of disability in the world [1]. The manually procedures executed for patient's rehabilitation affected by stroke, demands efforts during the execution of repetitive therapeutics routines, which cause tiredness of the physiotherapist, undesirable and imprecise movements that can make worse the injury [2]. The Medical Robotic and Rehabilitation Engineering areas have generated relevant advances in the process rehabilitation and people´s assistance with several motors limitations, such as upper limb exoskeleton [3]-[5]. In this way, several works of upper limbs exoskeleton using sEMG as primary information are reported. The SUEFUL-7 and NEUROExos exoskeleton are examples of these
II. MATERIALS AND METHODS A. Robotic system
Robotic exoskeleton
Control Board BoardTarjeta de Bioinstrumentation system
User interface
Control system
Fig. 1 Blocks diagram of robotic system for upper limb. B. Biomechanics analysis of the upper limb In accordance with the multidisciplinary group which include medical specialists, the passive control was adopted in a first stage the passive control, where the exoskeleton generates movements of 4-DOF, in accordance with preestablished routines in the rehabilitation therapy. The
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movements considered were shoulder flexure and extension, elbow flexure and extension, forearm pronation-supination, and humerus internal-external rotation. Considering the anthropometrics characteristics of the patients (weight and height) and in accordance with Winter and Braune works [8, 9], the gravity centers, weight of arm, forearm and hand were obtained. From this data, the virtual model of the upper limb was constructed. According to the characteristics of the standard patient, height (from 1.4 to 1.85 m) and weight (from 50 to 100 kg) calculations were executed. For each DOF, the necessary torques to move every single articulations were calculated, assuming that a standard patient generates a normal torque, the same torque was used for the static analysis of the forces with the extended arm (see Figure 2). Considering the moments produced by an arm and armor weight, the necessary power to move each articulations was determined, selecting in these cases step by step motors. The turn velocity of each articulation was estimated to 25º/seg as mean value.
extensive analysis of different load states and it outline predetermined conditions to tests the behavior of the mechanical structure. As result, it was estimated its real resistance further needs of many expensive prototypes. Table 1 shows the results of the simulations in critical parts of the armor as well as in whole armor. Table 1. Results of ANSYS and SolidWorks simulations. Element Gearwheel of humerus rotation mechanism Intermediate gear Serrated splint Prono-supination gearwheel Armor
Selected material Nylon 6/10
Maxim tension(MPa) 25.6 – 57.6
Security factor 2.41
Nylon 6/10 Teflon PTFE
20.1 - 35.4 11.5 - 25.8
3.05 11,6
Nylon 6/10
1.4 - 3.1
10-15
Nylon 6/10, Teflon PTFE, stainless steel AISI 304, aluminum 1060, bronze SAE 64, Steel AISI 1020
106.6
1.7
D. Control system
Fig.2 Torque determination for each articulation and DOF. a) Shoulder flexure and extension, b) elbow flexure and extension, c) forearm pronation-supination, d) humerus internal-external rotation.
C. Armor mechanic design According with design criteria of mechanical resistance, little weight, smooth and noiseless work, the kinematic and structure of a gearwheel and sliding arms was modeled. Figure 3 represents the CAD model of armor with the main elements.
The exoskeleton control was divided in two stages: (1) the board control the actuators or start motors of robotic arm, by pre-established routines commands in control software (passive mode); (2) the control’s established from movement’s intentionality detection, using a bioinstrumentation system for real time acquisition and processing of sEMG (active mode). The motors control board is composed by one microcontroller, one driver and an interface device with the workstation. The motors used in this implementation were hybrid-bipolar motors working in microsite mode. This allows greater precision control of robotic arm position and benefit to relationship between torque and size/weight. Due to the configuration of these bipolar motors it is necessary for its control a driver that incorporates a control circuit (H bridge) and power elements. The microcontroller enables the power transference of pulses to drivers from workstation information by USB interface. E. Bioinstrumentation systems
Fig. 3 CAD model of armor. Using the simulations framework provided by ANSYS MULTIPHASE professional package and SolidWorks software the mechanic armor was developed. It allowed an
The bioinstrumentation system allows the sEMG acquisition to be processed through of three blocks: the sEMG bioamplifier, the principal module and the data acquisition module. The sEMG bioamplifier allows the capture (Ag-AgCl electrodes) and amplification of the myoelectric signals of human body through a bipolar configuration; the principal module conditioning the sEMG signals and other signals, coming from inertial measurement unit (IMU) to data acquisition block. Furthermore, this
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module has optocouplers for optic isolation (7500 Vpeak/s) between patient and data acquisition block. The data acquisition block is constituted by PCI-6221 board, which allows to acquire 16 analog channels and 24 bidirectional digitals channels with high precision, velocity (250kS/s) and resolution (16 bits). This board makes possible to analyze and visualize data without programming, through LabView Signal Express software. The bioinstrumentation system provides several channels that can be used for the analysis the articulations movements from upper limb, which can be compared throughout the whole of the rehabilitation process, allowing the patient evolution. The sEMG signals are captured and conditioning using the instrumentation amplifier AD620 and a bandpass filter of second order with bandwidth from 20 Hz to 500 Hz. The AD620 have a high input impedance (10 GΩ || 2 pF) and common mode reject ratio (93 dB as minimum value at G = 10), which allows reduce the common interference sources and improves the signal-noise ratio. The bandpass filter was employed with the operational amplifier LM324 for selects the principal components of the sEMG signals and reduce the undesirable components. In addition, the LM324 was used to implements a leg drive circuit and variable gain amplifier (using a digital potentiometer MAX5479). The leg drive circuit is employed for reduce the common interference generated through the capacitive connection between patient and electrical net. The kinematic information of upper limb can be measured through a unit motion inertial formed by one 3axis accelerometer (ADXL345) and gyroscope (ITG3200), which have an I2C serial interface for provide the communication with others devices and sends the data with format of 16 bits. The isolation between patient and power source is guaranteed by optocouplers and ISO amplifiers with a minimum value of 1500 Vrms.
The controller used for SERIE/USB interface is the UMFT234XD module. Figures 4 and 5 show the robotic exoskeleton for upper limb and its control system, respectively.
Fig.4 Exoskeleton system for passive control mode.
Fig. 5 Control system of the proposed exoskeleton. a) power supply and controllers; b) motors control board.
The bioinstrumentation system (see Figure 6) allows capture and conditioning of the sEMG signals through 8 channels, as well as the digital signals generated from 4 IMU (one accelerometer and one gyroscope on each unit) and the analog signals obtained from 4 optional sensors.
III. RESULTS AND DISCUSSION Figure 4 shows the robotic exoskeleton for upper limb and their control system, as the first stage. The selected motors for actuators correspond to PK series, VEXTA brand; which offer balanced performance, providing a high torque, low vibration and noise. It has a 0.25º pass angle, 250 rpm maximum value of velocity and a power supply of 24 VDC. The controller selected was an M542 V2.0, which has a high performance, and work in microsite mode, based in the control technology of pure sinusoidal current. This technology allows the control of motors with low noise and temperature levels, and better performance at high speed than others commercial controllers. Furthermore, this device provides an output current of 4.2 A, and pulses that can reaches frequencies below of 300 kHz. The PIC18LF4620 microcontroller is used, which offer a high performance computational, and a good capacity of program memory.
Fig. 6 Bioinstrumentation module for the active control of exoskeleton.
To provide the adecuate functioning of the bioinstrumentation system, bioamplifiers should be tested in respect of frequency response, offset and saturation voltages, and the common mode rejection ratio (CMRR). The theoretical frequency response for sEMG signals goes from 20 to 500 Hz, and the relevant information is around of 20 to 360 Hz [10]. In this case, the saturation voltage is the maximum value in the input of sEMG channel, which causes the channel output, reaches the power supply value. Because the offset voltage is added to sEMG signals, the saturation voltage is obtained subtracting the offset voltage from measured saturation voltage. The
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instrumentation amplifier AD620 provides a minimum value of CMRR equal to 93 dB, in accordance with De Lucas recommendation [11]. The frequency response, offset and saturation voltages and the CMRR value were obtained using the DG4102 Waveform Generator RIGOL and DS1204B Oscilloscope RIGOL (see Table 2). To measure the frequency response of the sEMG bioamplifier a sinusoidal signal was applied a sinusoidal signal to the AD620 amplifier input, which was configured in differential mode. The amplitude level of sinusoidal signal was constant and the frequency was 1 Hz. Therefore the amplitude value was measured the amplitude value at bioamplifier output and the system gain was calculated. Changing the frequency from 1 Hz to 1 kHz, measuring the bioamplifier output and calculating the system gain it obtained all necessary information to know the frequency response of analyzed system. Table 2. sEMG channels characteristics. Amp. 1 2 3 4 5 6 7 8
Frec. Response (Hz) 21-458 23-452 21-452 21-450 20-445 22-455 20-458 20-464
Measured Volt.Sat (mVpp). 52 56 53,6 54 54 50 52.8 51
Volt.offs et (µVpp) 510 1000 650 300 1200 1300 160 600
Real Volt.Sat (mVpp). 51,49 55 52,95 53,97 52,8 48,7 51,2 50,4
IV. CONCLUSION In the first stage of this work, the adjustment of biomechanics system from passive mode control and the bioinstrumentation system was realized. The robotic rehabilitation system will allows to medical staff, to performe its work with higher effectiveness in the patient with muscular dysfunction. From another point of view, an additional benefit that provide the device is the possibility to take a chronological control of patient evolution. The prototype ensures the movement reeducation function by using a programmable routine bank, where predefined movement routine are stored in memory. Further work will include the design and build of a robotic arm active control as a second stage. V. REFERENCES 1.
CMRR (dB) 92.39 92.30 92.35 92.37 92.32 92.38 92.33 92.36
In the passive control of the upper limb exoskeleton the LabWindows Professional Package for programming the user interface was used which provides rehabilitation routines for the follow movements (single or combined): shoulder flexure-extension, elbow flexure-extension, humerus internal-external rotation and forearm pronationsupination. This user interface provides 10 routines of rehabilitation, which can be programmed to several velocities of execution and repetitions, as shown Figure 7.
Noticias Bayer. Ataque cerebrovascular es la segunda causa de muerte en Chile. Bayer: Science for a better life. Disponible en: http://www.bayer.cl/noticias/noticias_ver.php?id=815. Acceso: 16 de junio de 2014. 2. Banchs R., Llacuna J. Riesgos en trabajos de fisioterapia. Instituto Nacional de Seguridad e Higiene en el Trabajo. Número 73. 2011. Barcelona, España. 3. Hui Yan. Controlling a Powered Exoskeleton System via Electromyographic Signals. Proceedings of the 2009 IEEE International Conference on Robotics and Biomimetics December 19 -23, 2009, Guilin, China. 978-1-4244-4775-6/09, p 349-353. 4. Staubli, Patricia. Effects of intensive arm training with the rehabilitation robot ARM in II in chronic stroke patients: four single-cases. Journal of NeuroEngineering and Rehabilitation 2009, 6:46, doi: 10.1186/1743-0003-6-46. 5. Lo HS, Xie SQ. Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects. Med Eng. Phys (2011), doi:10.1016/j.medengphy.2011.10.004. 6. GopuraRARCKiguchi K, Li Y. SUEFUL-7: A 7DOF upperlimb exoskeleton robotwith muscle-model-oriented EMG-based control. IEEE/RSJ International Conferenceon Intelligent Robots and Systems, 2009. IROS (2009). Oct. page, 1126-31. 7. Lenzi, T. De Rossi SMM, Vitiello N, Carrozza MC. Proportional EMG control for upper limb powered exoskeletons. ConfProc IEEE Eng Med Biol Soc. (2011)., 2011,628-31. 8. Winter, D.A. Biomechanics of human movement. John Wiley and Sons, New York, 1979. 9. Braune, W. Determination of body moments of inertia of the human body and its limbs. Springer- Verlag, 1988. 10. Merletti R, Parker P. Electromyography-Engineering, and NonInvasive Applications. John Wiley Publisher, 2004. 11. De Luca, C.J. Electromyography. Encyclopedia of Medical Devices and Instrumentation, (John G. Webster, Ed.) John Wiley Publisher, 98-109, 2006.
Fig.7 User interface for rehabilitation routines programming in passive mode.
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