Intelligent System

Human Gait

Movement is one of the basic characteristics of living creatures. The human movement is one of fundamental capabilities in humana life. By the movements, human can do interaction with others and environments to meet daily needs. In everyone, movement affects how basic functions of life can be accomplished. It is generated by complex mechanisms of neural command to a group of skeletal muscles to exert sophisticated contractions to move skeletal system with a certain coordinated interaction with environment under internal (proprioceptive) and external (visual) feedback. Doing movements affects the physical, cognitive, and social in individual level. That way it is very important in developing basic and advanced physical skills, and maintaining physical and mental health. Understanding about complex mechanisms in human movements including muscle activity pattern during a certain movement has impact in clinical such in movement development, movement pathology, and rehabilitation. However invasive study is impractical and EMG signal is difficult to understand due to large intra and inter-subject diversity. on the other side, knowledge development about human movement is built based on computer simulation using a set mathematical models.

The human movements are produced by contractions of the skeletal muscles that develop enough forces to move limbs against the external loads. In a voluntary movement, the command signals from the central nervous system (CNS) are transmitted through the spinal cord to the skeletal muscle groups to develop active muscle forces. The normal muscle function requires an intact connections among the CNS, the spinal cord, and the skeletal muscle. Damage of the brain or the spinal cord injury (SCI) interrupts the command signals in reaching the skeletal muscle fibers. The human with brain damage or SCI have lost of motor control resulting in lose of the functional movements in daily life such as standing, locomotion, reaching. Types of the paralysis are classified as Monoplegia, Diplegia, Hemiplegia, Paraplegia, and Quadriplegia (Cleaveland Clinics Health Library, ”Paralyis”, https://my.clevelandclinic.org/health/diseases/15345-paralysis). Definition of each type of the paralysis is given below,

(a) Monoplegia: paralysis of only one limb that is caused by isolated damage of the CNS or the peripheral nervous system (PNS)

(b) Diplegia: paralysis of same body region on both sides of the body, i.e. both arms or both sides of the face)

(c) Hemiplegia: paralysis of one side of the body. This paralysis is caused by damage of the brain. mainly caused by the damage of the cerebral palsy

(d) Paraplegia: paralysis of both lower limbs and trunk caused by the damage of the spinal cord

(e) Quadriplegia: paralysis of four limbs and trunk that is caused by the damage of the spinal cord.

In a person with paralysis, absence of command signals from the CNS can be substituted by the artificial electrical stimulation to the peripheral nervous system or the muscle. This electrical stimulation acts in the same way as the electrical impulse from the CNS, resulting in muscle contractions and causing movements or sensations. This method is called the functional electrical stimulation (FES), with the aim of providing muscular contraction and producing a functionality of useful movement (Kralj and Bajd, Functional electrical stimulation: standing and walking after spinal cord injury, CRC Press, Boca Raton, 1988). Clinically, FES is used as an orthotic aid with therapeutic effect. FES generates an indirect control of muscle contraction and movement and contributes to neurologically normalization of the impaired motor system due to the programmed and repeated pattern of the motor response (Vodovnik, et. al., ”Functional electrical stimulation for control of locomotor systems,” CRC Crit. Rev. in Bioengineering, pp. 63-131, 1981). Clinical researches pertaining to the FES involve restoration of some types of the human movement in the everyday activity such as grasping (Handa, ”Current topics in clinical functional electrical stimulation in Japan”, J.Electromyogr. Kinesiol., vol. 7, pp. 269-274, 1997; Hoshimiya et. al., ”A multichannel FES system for the restoration of motor function in high spinal cord injury patients: A respiration controlled system for multi-joint upper extremity”, IEEE Trans. Biomed. Eng., vol. 36, pp. 754-760, 1989), standing and walking (Kralj and Bajd, 1988; Hoshimiya et. al., ”Functional electrical stimulation: standing and walking after spinal cord injury”, CRC Press, Boca Raton, 1989).

Gait is one of the cyclic movements. Each gait cycle is divided into two phases, the stance phase and the swing phase. In a certain sub phase of the gait, the movements of the joints reach certain joint angles (e.g. maximum knee flexion angle of swing phase, maximum ankle dorsifelxion angle of swing phase, ankle joint angle at initial contact). Gait cycle is a single sequence of events between two consecutive initial contact of the same limb. A single cycle of the gait is detected between two successive events of contact of the foot with the ground of the same leg. The gait cycle is illustrated in Figure 1 by an example of the level gait. Contact of the foot with the ground is illustrated by the output signal of the force sensitive resistor (FSR) measured from an experiment. Gait phases is divisions in the gait cycle that represent particular functional patterns. The gait cycle is divided into two phases, the stance phase and the swing phase. The stance phase was defined as time interval when the foot is contacting with the ground. The swing phase was defined as time interval when foot is not contacting with the ground. Initial contact (IC) is the point in the gait cycle when the foot initially makes contact to the ground. Heel off (HO) is the point in the stance phase at the heel leaves the ground. Foot flat (FF) is the point in the time in stance phase when the foot is in plantar grade. Toe off (TO) is the point in the gait phase at foot leaves the ground with the toe.

Figure 1. Gait phases

©arifin.tech@dmin

Level Gait Database
The gait parameters consisted of temporal parameters and the joint angle parameters. Statistical analysis was performed to obtain profiles of the joint angle trajectories, temporal parameters, and joint angle parameters. Coefficient of variation (CV)of each parameter value was calculated to express relative dispersion of the parameter value from the average.

Table 1. Gait temporal parameters

Figure 3. Joint angle parametersof level gait

Table 2. Temporal parameter values of level gait

Table 3. Joint angle parameters of the level gait
(a) Hip joint angle

(b) Knee joint angle

(c) Ankle joint angle

Table 4. Temporal parameter values of stair ascending gait

Table 5. Joint angle parameters of stair ascending gait
(a) Hip joint angle

(b) Knee joint angle

Table 6. Temporal parameter values of stair descending gait

Table 7. Joint angle parameters of stair descending gait
(a) Hip joint angle

(b) Knee joint angle

Framework of the Cycle-to-Cycle Control of Swing Phase Gait
The human movements induced by FES require the appropriate control method that can restore the desired functional movements. However, controlling the FES-induced human movements is difficult and complex due to the non-linearity of nuero-muscular system response [6]-[8], variability of response of the stimulated muscle [9], significant time delay, and muscle fatigue [7], [9]-[13]. Because of high inter-subject variability, in practical FES, the appropriate stimulation data should be identified on an individual basis of subject. The identification task needs a complicated experimental approach [8]. In surface FES, the identification task has to be performed at every rehabilitation session.

FES is expected to improve gait ability of the paralyzed gait. There are a large number of paralyzed gait subjects in the world. Basic gait ability of paralyzed subjects can be improved by foot drop correction and controlling of the swing phase. The stance phase can be improved by stimulating the knee extensor to stabilize the knee joint and by using the assistive equipment such as crutches. The Swing phase is important for forward propulsion during gait.

The movements of lower limbs during gait are complex multi-joint movements and involve interaction among the segments of the limbs. Restoration of the paralyzed gait using FES needs a sophisticated control strategy. Many researches proposed and tested certain control methods for the FES-induced gait. A brief review of the current method of the FES gait control is presented in section 1.1. The present study investigates realization of the cycle-to-cycle control method for FES-induced hemiplegic gait through computer simulations. Descriptions of the cycle-to-cycle control and necessity of this study is shown in section 1.2, following by objective and scope of this study in sections 1.3 and 1.4, respectively.

During swing phase, the swinging leg makes a forward step. The stance leg supports the swing phase by bearing the body and producing forward propulsion. A successful swing phase maximizes the forward propulsion of the body and results in an effective walking with sufficient stride length. In the FES-induced gait, all the lower limb joints must be activated to produce an effective swing. Thus, the paralyzed gait will be more improved than just make stepping without any electrical stimulation.

Using the cycle-to-cycle control method, although the controlled joint angles are not controlled to follow the desired joint trajectories continuously, they can be restored by mimicking the joint movements of the normal gait. Knowledge about joint movements during swing phase and muscle function is required to establish a concrete framework to realize the cycle-to-cycle control. Such knowledge about the joint movements can be elicited from the data of the normal gait measured and analyzed in section 2.2 as shown in Figure 2.10. Nine muscles around the lower limb joints were considered to be electrically stimulated to produce movements of the hip, knee, and ankle joints in restoring the swing phase. The stimulated muscles of the lower limb are shown in simplified feature in Figure 2.11. The muscles included the bi-articular muscles, i.e., the bicep femoris long head (BFLH) as knee flexor and hip extensor, the rectus femoris as knee extensor and hip flexor, and the gastrocnemius medialis as ankle plantarflexor and knee flexor. The vastus lateratis, the vastus medialis, and the vastus inter-medialis were considered as one muscle group. Functions of the muscles listed in Table 2.9 are summarized from anatomy and physiology literature (W. Kapit and L. M. Elson, ”The anatomy coloring book”, 3rd Edition, ISBN 0-8053-5086-1, Benjamin Cummings, Ny, 2002).

In this study, we simulated the electrical stimulation which was delivered using surface electrode (surface FES). Using the surface electrodes, only large muscles can be stimulated. Although EMG data of all lower limb muscles are avaibale, it is impossible to stimulate all the muscles by surface electrodes. As reported by Kobetic and Marsolais [17], an initial stimulation pattern based on the EMG pattern could not generate appropriate joint angle trajectories. The stimulation pattern was refined through several manual corrections during experiment. In this study we tried to find other method of creating stimulation pattern for FES gait based on knowledge of joint movements during a particular gait phase and muscle functions.

Figure 3.Mechanism of the cycle-to-cycle control method

Control of the joint movements during the swing phase is aimed to generate the joint movements mimicking the joint movements of the normal gait as shown in Figure 2.10. During the swing phase, the joints move in a number of motion arcs. The peak of each motion arc was assigned as the target joint angle, i.e., the maximum knee flexion, the maximum ankle dorsiflexion, etc. The stimulation of the muscles related to the joints induces the movements reaching the target joint angles. The target joint angles were determined by the average values of the joint angle parameters of the swing phase of the normal gait in section 2.2. Parameter Δθ was introduced in order to evaluate whether the target joint angle was reached or not. The ¢µ of each controlled joint angle was set by average value of intra-subject standard deviation of each target joint angle from the gait analysis. The values of the target joint angles and the Δθ are shown in Table 2.10. Initial positions of the hip, knee, and ankle joints of the controlled swing phase are marked by asterisks. They are the maximum hip flexion of the stance phase, the maximum knee extension of the stance phase and the maximum ankle dorsiflexion of the stance phase. From the initial positions, the ankle, knee, and hip joints move to the maximum ankle plantarflexion angle, the maximum knee flexion, and the maximum hip flexion angle, respectively. The ankle joint dorsiflexes from the maximum ankle plantarflexion angle to the maximum ankle dorsiflexion angle. Then it plantar flexes until reaching the initial contact angle. The knee joint extends from the maximum knee flexion angle to the maximum knee extension angle at the end of the swing phase. From the maximum hip flexion angle, the hip joint gradually extends till the end of the swing phase and reaching the target of the hip joint angle at initial contact. Principally, the flexion and extension of the hip and knee joints can be induced by stimulating the flexors and the extensors of the joints, respectively. Gradual extension of the hip joint at the end of the swing phase can be generated by stimulating of the hip flexor to avoid aburpt hip extension by the gravity torque. Dorsiflexion and plantarflexion of the ankle joint can be activated by stimulating the ankle dorsiflexor and plantarflexor, respectively.

Source: Chapter 2: Establishment of Framework of the Cycle-to-Cycle Control, in ”A Computer Simulation Study on the Cycle-to-Cycle Control Method of Hemiplegic Gait Induced by Functional Electrical Stimulation”, Achmad Arifin, Tohoku University Doctoral Thesis, Supervisors: Prof. Dr. Nozomu Hoshimiya, Assoc. Prof. Dr. Takashi Watanabe, 2005.

Intelligent System

Achamd Arifin, Ph.D.

Human Gait