Design and Feasibility Study of a Leg-exoskeleton Assistive Wheelchair Robot with Tests on Gluteus Medius Muscles

Abstract

The muscles of the lower limbs directly influence leg motion, therefore, lower limb muscle exercise is important for persons living with lower limb disabilities. This paper presents a medical assistive robot with leg exoskeletons for locomotion and leg muscle exercises. It also presents a novel pedal-cycling actuation method with a crank-rocker mechanism. The mechanism is driven by a single motor with a mechanical structure that ensures user safety. A control system is designed based on a master-slave control with sensor fusion method. Here, the intended motion of the user is detected by pedal-based force sensors and is then used in combination with joystick movements as control signals for leg-exoskeleton and wheelchair motions. Experimental data is presented and then analyzed to determine robotic motion characteristics as well as the assistance efficiency with attached electromyogram (EMG) sensors. A typical muscle EMG signal analysis shows that the exercise efficiency for EMG activated amplitudes of the gluteus medius muscles approximates a walking at speed of 3 m/s when cycling at different speeds (i.e., from 16 to 80 r/min) in a wheelchair. As such, the present wheelchair robot is a good candidate for enabling effective gluteus medius muscle exercises for persons living with gluteus medius muscle disabilities.

Keywords: EMG signal; assistive robots; master-slave control; muscle exercises; pedal-actuated wheelchair.

Figure 1

Conceptual design of wheelchair robot with leg exoskeletons: (a) Kinematic design; (b) DOF configuration.

Figure 2

CAD design solution for novel leg-exoskeleton wheelchair robot: (a) Full mechanical design; (b) Hip joint adjusting mechanism.

Figure 3

Control strategy flowchart for the wheelchair robot in Figure 1 and Figure 2.

Figure 4

Control design scheme of leg exoskeleton assisted wheelchair.

Figure 5

Newly developed wheelchair prototype with linked exoskeletons from the Intelligent Robot Institute at the Beijing Institute of Technology per designs in Figure 1 and Figure 2.

Figure 6

Still images from feasibility experiment during one complete cycle of the crank in different positions: (a) Top dead center; (b) Power phase; (c) Bottom dead center; (d) Recovery phase.

Figure 7

Forces on pedal during experimentation as shown in Figure 6.

Figure 8

EMG sensor positions at left and right gluteus medius muscles locations.

Figure 9

Frequency of activated EMG values during tests (Table 1).

Figure 10

Recorded EMG signals at left and right sides of gluteus medius muscle locations during walking motion: (a) Left side; (b) Right side.

Figure 11

Recorded EMG signals at left and right sides of gluteus medius muscle locations during active cycling: (a) Left side; (b) Right side.

Figure 12

Recorded EMG signals at left and right sides of gluteus medius muscle locations during passive cycling: (a) Left side; (b) Right side.

Figure 13

Mean and maximum recorded EMG signal values at left gluteus medius muscle location for each active event during walking and pedaling in passive mode at 16 r/min: (a) Mean value at left side; (b) Maximum value at left side.

Figure 14

Mean and maximum recorded EMG signal values at left gluteus medius muscle locations for passive pedaling at different speeds: (a) Mean value at left side; (b) Maximum value at left side.