A Sensor Subsystem of An Exoskeleton

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1 A Sensor Subsystem of An Exoskeleton S.A. Mineev 1, V.A. Novikov 2, I.V. Kuzmina* 3, R.A. Shatalin 4, I.V. Grin 5 Lobachevsky State University of Nizhniy Novgorod, 23 Gagarin Avenue, Nizhniy Novgorod, Russia. Corresponding Author Abstract The results presented in this paper report on the development of a medical exoskeleton sensor subsystem. The sensing subsystem consists of a MEMS sensor, a group of magnetic angle sensors and a solution to the issues of interfacing angle sensors with the control system of the exoskeleton. Experiments with the prototype of the sensing subsystem described here demonstrate its potential for medical exoskeletons applications. Keywords: Exoskeleton, Magnetic angle sensor, MEMS sensor, Positioning Accuracy Introduction The use of medical exoskeletons in rehabilitation shows immense potential. Several medical exoskeleton have become available for this purpose, including Rewalk, HAL, and Locomat [1, 2]. Most feature systems with 6 degree-offreedom (DOF) [1]. However, one of the problems facing designers of such systems is providing vertical stability to a patient during walking. This problem is typically not addressed and the patient-operator himself must provide their own vertical orientation. This makes these solutions unsuitable for patients who have limited control of their lower extremities. To solve the issue of vertical stability and to provide anthropomorphic gait, it is necessary to know the state of the bio-mechanical system for a exoskeleton with 6 DOF, its spatial orientation and relative positions of all extremities of the system. In the case of exoskeletons and similar systems the state vector can be defined as a set of angles between the gravity vector and longitudinal axis of the extremities: the feet foots (left and right), the legs (left and right), the thighs (left and right) and the torso. In recent papers two alternative approaches were used to determine an exoskeleton state vector: а) All moving parts of an exoskeleton are equipped with 3-axis accelerometers, gyroscopes and magnetometers. The state vector of the system can be calculated from sensor readings [3, 4]. Microelectromechanical systems (MEMS) with embedded accelerometer, gyroscope and magnetometer are applied as sensors [5]. But the use of many MEMS sensors adds to the price of an exoskeleton and requires complex calibration. For this reason this solution is not widely applied. b) Alternately, just one of the components of the exoskeleton is equipped with a MEMS sensor (3-axis accelerometer, gyroscope and magnetometer). It can be included as part of the system linked to the torso, for example. All joints (ankles, knees and hips) are equipped with absolute angle sensors. Resistive or optical angle sensors were applied as angle sensors [6, 7]. The data set provided by the output of the of MEMS sensor together with that of the angle sensors allows the current state of the exoskeleton to be determined. An application of just one MEMS sensor with several simple angle sensors is the most widespread approach, combining high accuracy with low production and operational costs. However, there are two problems of this approach: high noise level in readings of resistive or optical angle sensors during walking, plus low reliability of sensors which ware out quickly as they are located directly on shafts connected to moving parts of the exoskeleton. Here we present a sensing subsystem that consists of a MEMS sensor with a group of magnetic angle sensors. Magnetic angle sensors are beneficial as they are contact less, and so reduce noise and increase reliability. Hardware challenges and that of the embedded software design are discussed here and we provide results regarding the accuracy of the experimental measurements of the exoskeleton state vector and the maximum polling rate of the sensors. Hardware, software and methods Requirements and restrictions The experimental medical exoskeleton for lower extremities, which was designed in the Lobachevsky State University of Nizhny Novgorod, consisted of the four parts: - a mechanical chassis; - a sensor subsystem; - an actuator subsystem; - a control system. The sensor subsystem needed to provide measurements of the exoskeleton state vector, which must be defined by: - angle of deviation of the longitudinal axis of the torso from the gravity vector in the sagittal plane Θ s; - angle of deviation of the longitudinal axis of the torso from the gravity vector in the frontal plane Θ f; - bend angles of knee joints in the sagittal plane: Θ kl (left), Θ kr (right); - bend angles of hip joints in the sagittal plane: Θ hl (left), Θ hr (right); - bend angles of ankle joints in the sagittal plane: Θ al (left), Θ ar (right). 6725

The system of the angles defining the state vector of the exoskeleton is presented on Fig. 1. The state vector of the exoskeleton must be measured with sampling frequency higher than 100 Hz.

2 The system of the angles defining the state vector of the exoskeleton is presented on Fig. 1. The state vector of the exoskeleton must be measured with sampling frequency higher than 100 Hz. The accuracy of angle determination must be better than ±1. The control system of the exoskeleton was based on the BeagleBoard XM microprocessor board by Texas Instruments [9] that featured: - 1 GHz ARM Cortex-A8 processor; Mb RAM; - TMS320C64x+ DSP processor. The Angstrom Linux distribution was installed on the board. The sensor subsystem was connected to the microprocessor board by the SPI interface (1.8 V logic level) routed to the Main Expansion Header [9]. _ g _ g өs өf өhl өhr өkl өkr өal өar Figure 1: The system of angles defining the state vector of the exoskeleton The sensor subsystem of the exoskeleton Analog Devices ADIS16407BMLZ [8] MEMS sensor, which combines the functions of precision accelerometer, gyroscope and magnetometer, was chosen as the sensor to register the deviation of the exoskeleton torso axis from the gravity vector. The MEMS sensor supported the 3 volt logic level compatible SPI interface and could be polled with frequency of up to 819 Hz. It was placed on the lower back of a patientpilot in the area of the lumbar spine. The PRAS21 analog sensors equipped with PRMAG22 magnets [9] were selected as inangle sensors to determine the bending angles of each of the exoskeleton joints. The sensors were installed in the knee, hip and ankle joints of the exoskeleton. The gap between the sensor and the magnet could be up to 10 mm and misalignments could be as much as 0.5 mm, which simplified the joint design considerably. An example of the placement of the PRAS21 sensor together with the magnet in the exoskeleton joint is demonstrated on Fig. 2. Figure 2: The placement of the angle sensor into the knee joint of the exoskeleton. 1 PRMAG22 magnet, 2 PRAS21 sensor It should be noted that the exoskeleton chassis was made of aluminum alloy and the massive final-stage steel gear of the joint reducer was installed on the same shaft as the PRMAG22 magnet. The analog signal output of the PRAS21 sensor did not exceed 5v and was proportional to the rotational angle of the magnet relative to the fixed sensor. Taking into account all the controlled joints, six analog signals had to be digitized and supplied into the control system. The sampling frequency of the analog signals has to be greater than 100 Hz. A board was designed to capture the analog signals, digitize them and interface the sensor subsystem with the BeagleBoard XM microprocessor board. The functional diagram of the interface board (IB) is shown on Fig. 3, and the general scheme of the sensor connections to the microprocessor board is demonstrated on Fig

Figure 3: Functional diagram of the interface board The main purpose of the interface board was to digitize analog signals from the inangle sensors and provide connections between SPI interfaces of

..5 V Analog Devices ADIS16407 Figure 4: The general scheme of interfacing the BeagleBoard XM microprocessor board with the external sensors Analog sensor signals were supplied to the ADC inputs

The multiplexer control signals sourced from general purpose input-output (GPIO) pins of the main expansion header of the microprocessor board were put through buffers.

3 Figure 3: Functional diagram of the interface board The main purpose of the interface board was to digitize analog signals from the inangle sensors and provide connections between SPI interfaces of the MEMS sensor, MCP3208 analog to digital converter (ADC) with logic level of 3 V and the SPI interface of the microprocessor board with logic level of 1.8 V. BeagleBoard XM V V V SPI (1,8 V) IB SPI (3 V) V V V Analog Devices ADIS16407 Figure 4: The general scheme of interfacing the BeagleBoard XM microprocessor board with the external sensors Analog sensor signals were supplied to the ADC inputs through the two SN74LV4051 analog multiplexers and the Chebyshev low-pass filter of 4th order based on the AD8542 dual operational amplifier with cutoff frequency of 5 khz. The multiplexer control signals sourced from general purpose input-output (GPIO) pins of the main expansion header of the microprocessor board were put through buffers. For coupling logic levels of the SPI interfaces, SN74LVC125APW buffers with 3-state outputs were used which, in addition to level conversion, provided necessary load capacity. On the microprocessor board, the SPI3 lines of the main expansion header were connected through the buffers to the SPI interfaces of the ADC and MEMS sensor. Selection of the device was implemented by means of the CS0 (ADC) and CS1 (MEMS sensor) lines. The sensor subsystem driver The sensor subsystem driver provided access to inputs/outputs of the main expansion header [9] for applications running on the Bealgeboard XM. The driver was designed to ensure: - connections of pins of the main expansion header to SPI/GPIO interfaces of the microprocessor. Such connections are often implemented by editing and rebuilding source files of the Linux distribution which is a time-consuming process. Instead the sensor subsystem driver operates registers of the DM3730 microcontroller [10] and changes pin multiplexer settings immediately without need of rebuilding the Linux kernel and its restarting; - settings of the spidev driver (standard driver of the Linux distribution) for operations with MEMS sensor and ADC MCP3208. The sensor subsystem driver registers SPI devices in operation system. After that the spidev driver is capable of controlling communications between the software and devices, and the software can access the output of the inangle sensors and the MEMS sensor through special files named as spidev3.0 and spidev3.1. The sensor subsystem programming interface An application-level library based on the sensor subsystem driver was developed. This library allowed data measurement of the deviations of exoskeleton torso from the gravity vector (in the sagittal and frontal planes) and six angles of bending joints (in the sagittal plane only). All data was received in dimensional units (degrees). There was a background process running behind the library frontend. The background thread continually polled the MEMS sensor and all of six angle sensors. The MEMS sensor delivered encoded data from the accelerometer, gyroscope and magnetometer. The readings of angle sensors were presented in 12-bit ADC code. The data was decoded and converted into the angles which, together with the current timestamp, provided the components of the exoskeleton state vector. This vector was updated with every polling cycle. The system of axes of the exoskeleton was defined as following: the Z axis coincided with the gravity vector (at rest), the X axis was placed in the sagittal plane of the exoskeleton and was parallel to the ground (at rest), and the Y axis was placed in the frontal plane of the exoskeleton and was parallel to the ground (at rest). Deviations from the gravity vector are calculated according to accelerometers readings:, (1) where, a x, a y, a z-projections of the acceleration vector on the X, Y and Z axes of the device. Deviations from the gravity vector also can be calculated according to gyroscope readings: 6727

4 (2) where ω x, ω y-projections of the angular speed on the X and Y axes of the device. The resulting value of the torso deviation angles were obtained by the combination of both indications of accelerometer (1) and gyroscope (2) by means of the complementary filter [11]: Results Tests of correctness and accuracy of the offered sensor subsystem was made by means of the DAF-001 digital protractor. (3) where k-the coefficient of complementary filter, which was determined by experiment. Calibration To calibrate a joint sensor of the exoskeleton, it was necessary to measure and save in a file the calibration dependence of the bending joint angle on ADC code. To measure an actual angle of a joint a DAF-001 digital protractor was used in the laboratory to simulate the exoskeleton and allowed us to measure bending angles with an error of no greater than 0.05 o. Figure 6: Accuracy of measurements of the bend angle of the right knee joint of the exoskeleton Experiments with the sensor subsystem prototype the demonstrated following: - the accuracy of measurements of the bend angle of all joints of the exoskeleton was better than ±0.6 (example of the accuracy of determination of the bend angle of the right knee joint of the exoskeleton in the range of the angle is presented in Fig. 6); - the accuracy of determination of the deviation of the longitudinal axis of the torso of the exoskeleton from the gravity vector in sagittal and frontal planes was better than ±1.0 (example of the accuracy of determination of the deviation of the longitudinal axis of the torso of the exoskeleton from the gravity vector in frontal plane in the range of the deviation angle is presented in Fig. 7). Figure 5: The calibration dependence for the right knee joint During this calibration process, the values of the bending angles of the joint and corresponding ADC codes were fixed. The measurements were made with a uniform step over the entire range of joint angles. Fixed pairs of the bending angle vs. ADC code were stored in a file as the calibration curve (each joint had its own file). An example of the calibration curve for knee joint is represented on Fig. 5. Subsequently, the stored calibration curves together with linear interpolation techniques were used to determine the bending angle at any given time. For the ADIS16407BMLZ MEMS sensor, this special calibration procedure was not required. Figure 7: Accuracy of determination of the deviation of the longitudinal axis of the torso of the exoskeleton from the gravity vector in frontal plane 6728

The polling frequency of all exoskeleton sensors was measured by means of the clock() function from the standard C++ library. The maximum frequency was 196 Hz.

Readings of the sensor subsystem were transmitted to a smart-phone which played the role of a remote control panel.

5 The polling frequency of all exoskeleton sensors was measured by means of the clock() function from the standard C++ library. The maximum frequency was 196 Hz. The sensor subsystem presented in this paper was applied in the experimental exoskeleton of Lobachevsky State University of Nizhny Novgorod. Readings of the sensor subsystem were transmitted to a smart-phone which played the role of a remote control panel. The experimental exoskeleton and the animated scheme of its state is presented on Fig. 8. Conclusion The use of magnetic angle sensors for determining the state vector of an exoskeleton increase the lifespan of joints of exoskeletons and locomotion rehabilitation systems, when compared to resistive and optical sensors, and reduces the influence of vibrations during the motion of biomechanical systems. The results achieved in this work demonstrate the effectiveness of a compact and inexpensive sensor subsystem consisting of a single MEMS sensor with a number of simple magnetic angle sensors. The sensor subsystem described in this paper provides an exoskeleton control system that runs on an embedded microprocessor platform in real time. The accuracy of the sensor subsystem is barely impacted by motion of the exoskeleton and is better than ±1.6 for absolute orientation angles. The polling frequency of all the exoskeleton sensors exceeds what s required to accommodate the biological threshold frequency of 100 Hz. The combination of constructive advantages, speed parameters and accuracy, suitable for both exoskeleton applications and locomotory training complexes, mean that the sensor subsystem describe in this paper could find use in a very broad range of medical biomechanical applications. Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper. Figure 8: The experimental exoskeleton and the animated scheme of its state A solution to the problem of vertical stability requires information about orientation of all extremities of the exoskeleton relatively to the gravity vector. This information can be derived from the summation of the deviation of the longitudinal axis of the torso of the exoskeleton and the gravity vector in the sagittal plane with the bend angle of the joint of the exoskeleton (Fig. 9). Figure 9: The record of time series of the absolute orientation of the left hip during walking Acknowledgements The results presented in this paper have been obtained under the project supported by Russian Ministry of Education and Science under Project ID RFMEFI57514X0031 and state assignment no Authors thank Gideon Summerfield for his quality corrections of this paper. References [1] I. Díaz, J. Gil, E. Sánchez, "Lower-Limb Robotic Rehabilitation: Literature Review and Challenges", Journal of Robotics, vol [2] S. Fisher, L. Lucas, T. Thrasher, "Robot-assisted gait training for patients with hemiparesis due to stroke", Top Stroke Rehabil, 18 (3) (2011), pp [3] L. Huang, J. Ryan Steger, H. Kazerooni, "Hybrid control of the Berkeley lower extremity exoskeleton (BLEEX)", in Proceedings of IMECE 2005, E International Mechanical Engineering Congress and Exposition, November 5-11, 2005, Orlando, Florida USA. [4] M. Hassan, H. Kadone, K. Suzuki, Y. Sankai, "Wearable Gait Measurement System with an Instrumented Cane for Exoskeleton Control", Sensors (Basel), 2014, January; 14(1): [5] C. Buffa, G. Langfelder, A. Longoni, A. Frangi, E. Lasalandra, "Compact MEMS magnetometers for inertial measurement units", Sensors 2012, 12,

6 [6] M. Bortole, "A robotic exoskeleton for overground gait rehabilitation", in Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, [7] H-T. Tran, H. Cheng, X.C. Lin, et al., "The relationship between physical human-exoskeleton interaction and dynamic factors: using a learning approach for control applications", Sci China Inf Sci, 2014, 57: 20201(13). [8] Ten Degrees of Freedom Inertial Sensor ADIS Data Sheet ical-documentation/data-sheets/adis16407.pdf (accessed on 18 June 2015). [9] BeagleBoard-xM Rev C System Reference Manual (accessed on 18 June 2015). [10] AM/DM37x Multimedia Device Silicon. Revision 1.x, Version O. Technical Reference Manual. Texas Instruments. SPRUGN4O, May 2010-Revised January [11] R. Mahony, T. Hamel, J-M. Pflimlin, "Complementary filter design on the special orthogonal group SO(3)", in Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005, Seville, Spain, December 12-15,

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