A Low-Cost Robotic Rehabilitation System for Children with Motor Impairment

Project Objectives:

The purpose of this project is to design a treadmill-based robotic rehabilitation machine small and cheap enough for patients to rent or buy to bring home during rehabilitation so that they don’t have to go to the hospital every day. The machine should be able to sense and react to the patient’s walking speed, helping the patient move his/her legs with proper gait on an assist-as-needed basis. It should have the capability for body-weight support. In addition, the machine should allow one degree of freedom for the motion of the patient’s center of gravity up and down, and two degrees of freedom for the motion of each foot in the sagittal plane. It should be as simple as possible while still providing quality therapy.

Sensor Requirements:

Various attributes of the robot-patient system need to be sensed. The treadmill belt speed (1), for example, needs to be known in order to be able to adjust the treadmill speed to the walking speed of the patient, especially in the first few steps when falling is most likely. In addition, the location of the ankle or foot (2) within the sagittal plane needs to be sensed in order to know how the patient is doing with respect to the desired gait pattern, and also what point in the stride he/she is at. The time at which the toe and heel of each foot contact the treadmill (3) are also important to know to ensure that the robotic system is synchronized with the patient’s gait and all is progressing correctly. The force produced by the machine (4) should be measured in order to provide feedback to the control system and allow accurate control of the patient’s gait. In addition, the force or muscle contractions produced by the patient (5) should be measured as a gauge for the patient’s progress. As a check on the accuracy of the body weight support system, the patient’s actual and effective weight (6) should be measured. Finally, the vertical position of the patient’s center of mass (7) needs to be known so that it can be actuated throughout the gait cycle in order to move up and down with the patient.

Recommendations to Meet the Sensor Requirements:

These recommendations are based on research into seven existing robotic gait rehabilitation systems: the Hokoma Lokomat, the Reha-Stim Gait Trainer, the MIT Anklebot, the HealthSouth AutoAmbulator, the Fraunhofer Institute Haptic Walker, the University of Twente LOPES, and the UPC/GRINS Self-Adjustable Speed Treadmill.

(1) As far as sensing the treadmill belt speed, it seems easiest to use an incremental encoder, as implemented by the UPC/GRINS group in their Self-Adjustable Speed Treadmill and described in the paper “Parameter acquisition for gait analysis in rehabilitation based on a self-adjustable speed treadmill” (2008). Typical treadmills are equipped with optical encoders, so it is likely that the treadmill upon which the rehabilitation device will be built already has an encoder that can be used. Buying a

new optical incremental rotary encoder can cost on the order of 350 € (from McMaster-Carr website).

(2) Various approaches have been documented to sense the location of the patient’s ankle/foot within the sagittal plane, the feasibility of many of which will depend on the chosen mechanical design.

The Hokoma Lokomat, for example, as documented in the paper “Locomotor training in subjects with sensori-motor deficits: An overview of the robotic gait orthosis Lokomat” (2010), uses potentiometers attached to the hip and knee joints of the robotic exoskeleton. These measure the angular positions of each joint, and thus infer the position of the ankle. This is a quite cheap option, with linear rotary potentiometers available as cheap as 2 €. It also makes feedback to the controller relatively easy, because the measured angle can be adjusted directly by actuation of the corresponding motor. However, since we don’t plan on using an exoskeleton in our design, we would need to find other attributes of the system that would allow us to measure joint angles and infer the ankle location.

The MIT Anklebot, documented in the paper “Robot-Aided Neurorehabilitation: A Novel Robot for Ankle Rehabilitation” (2009), uses a similarly design-dependent approach: linear incremental encoders to sense the ankle angle in two degrees of freedom. This type of sensor tends to be more precise than potentiometers, but is quite expensive, on the order of 300 €. It could be implemented similarly to the potentiometers mentioned above – if we had a system that allowed us to measure joint angles, we could infer the foot/ankle location.

A third approach that will only work with certain designs is to employ a strategic use of proximity sensors to determine the locations and angles of the robotic actuators and thus infer the position of the end contacting the patient’s ankle. For example, HealthSouth’s AutoAmbulator, documented in US Patent 7041069, employs a proximity sensor to sense a target mounted on each pulley that controls the first depending arm of each leg actuator assembly. Typical proximity sensors are in the range of 100 €.

An option that does not depend on the choice of mechanical design is to use a magnetic position tracker to find the locations of the feet. This is the approach used by the UPC/GRINS in their Self-Adjustable Speed Treadmill (article cited above), and it could be a good solution. It is difficult to find information on this option, so it would be good to ask the researchers themselves exactly how they implemented this solution, how much it cost, and whether they consider it appropriate for this application.

An additional non-design-dependent option, implemented in the LOPES exoskeleton system made by the University of Twente in Germany, is to use a videographic system to track LED markers placed on the limbs of the patient and the robot. The specific system used, the PTI-VZ4000 motion capture system from PhoeniX Technologies, is cited in the paper “Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation” (2007). Cost information is not available on the

Phoenix Technologies website, but this system is bound to be quite expensive (estimated at 1000 € or more), and it could also require complicated software to be able to translate the recorded motion into required actions for the robot.

(3) It seems that the best way to sense the time at which the toe and heel of each foot contact the treadmill is to use a system of pressure sensors, as described by the UPC/GRINS group in their paper about the Self-Adjustable Speed Treadmill, cited above. The described approach employs a pressure insole divided into nine pressure zones and placed inside the patient’s shoe. The sensors used are Force Sensing Resistors, analog devices which change their resistance depending on the force applied. These sensors are quite cheap, valued around 10 € (from Digi-Key website). For our purposes, we could just use two per foot – one to sense the contact of the heel with the treadmill, and one to sense contact of the ball of the foot.

(4) Here are some ideas to sense the force produced by the machine:

The Haptic Walker, designed by Fraunhofer Institute IPK in Berlin and described in the paper “Haptic Walker – A novel haptic device for walking simulation” (2004), has two programmable foot platforms with permanent foot-machine contact. Underneath each foot plate is a 6 DOF force/torque sensor to measure the forces and torques applied to the foot. The problem with this option is that this type of sensor is very expensive, on the order of 1000 €, and it gives an excess of information that isn’t really necessary for our purposes.

The LOPES takes a different approach, using potentiometers to measure the change in length of the springs used to actuate each of the arms of the exoskeleton, and thus calculate the applied force. This would only work if our design contained springs. As noted above, typical potentiometers cost around 2 €.

With the Lokomat, cited above, the knee and hip joint torques of the exoskeleton are measured by force sensors integrated into the orthosis in series with the linear drive motors. A typical load cell force sensor that could be used for this purpose is valued at around 50 €. In our design, these could be integrated wherever the motors are, or even at the points of contact with the patient, depending on the desired control system.

The Anklebot, on the other hand, also cited above, uses analog current sensors to sense the current drawn by each of the motors, and by extension, the motor torque. The exact sensor that they used is the Burr-Brown 1NA117P, but other analog current sensors are available at around 5 €. This approach makes a lot of sense, because it allows measurement of the direct output of the motors, making feedback and control simple.

(5) Some indication of the effort that the patient is making is necessary as feedback for both for the patient and the therapist. If the force required from the robot to produce a certain trajectory is measured, as discussed above, then the forces produced by the patient (or at least their change in magnitude over time) can be inferred. However, if we

want a direct way to measure the patient’s effort, the best way is to use electromyography (EMG) sensors.

In the LOPES, for example, as described in the article cited above, EMG sensors were used on the 8 major leg muscles in order to compare unimpaired walking with and without wearing the robot. In our case, these sensors could be placed on several muscles in the leg as a way to track patient improvement. EMG sensors are available for approximately 40 € each.

(6) The patient’s actual and effective weight can be measured by a dynamometer attached to the harness system. This type of apparatus is used in the Gait Trainer made by Reha-Stim and described in the paper “Driving electromechanically assisted Gait Trainer for people with stroke” (2011). In order to measure the patient’s actual weight, they can be asked to sit in the harness which hangs from the dynamometer. Later, the amount of body weight support can be continually measured by the dynamometer. The cheapest dynamometers with high enough weight capacity to hold a person are around 700 €.

(7) The vertical position of the patient’s center of mass is neither actuated nor sensed in most of the existing designs studied. The only device that did control the location of the patient’s center of mass was the Gait Trainer, as described in an addition paper, “A mechanized gait trainer for restoration of gait” (2000). The actuation system was designed around the information that, in an adult patient, the amplitude of the vertical oscillations of the center of mass with walking is typically only about 2 centimeters. Given that our device will be used for children, the amplitude of oscillations will be smaller, and, provided that the harness system is designed to have a slight spring-like character, vertical center of mass measurement and actuation will not be necessary.

Summary of Recommendations:

In summary, for sensing the treadmill belt speed (1), use of the treadmill’s own optical encoder is recommended. To sense the patient’s ankle location within the sagittal plane (2), it is recommended to look into magnetic position tracking to see if it is feasible and cost-effective. If not, potentiometers, linear incremental encoders, or proximity sensors could be viable and cheap options depending on the chosen mechanical design. Force Sensing Resistors are recommended to sense the time at which the toe and heel of the patient contact the treadmill (3). Analog current sensors are recommended as a measure of the torque applied by each motor (4), although potentiometers or load cells instead could provide a good measure of motor force if the mechanical design permits. If direct information about the patient’s effort (5) is necessary and the inferred information from the motor forces does not suffice, EMG sensors can be placed on the muscles in question. A dynamometer is recommended to sense the patient’s actual and supported weight (6). Finally, sensing of the location of the patient’s center of mass (7) is not deemed necessary, because during normal walking, it moves very little.