Stroke is a leading cause of neuromuscular system damages, and researchers have been studying and developing robotic devices to assist affected people. Depending on the damage extension, the gait of these people can be impaired, making devices, such as smart walkers, useful for rehabilitation. The goal of this work is to analyze changes in muscle patterns on the paretic limb during free and walker-assisted gaits in stroke individuals, through accelerometry and surface electromyography (sEMG).
The analyzed muscles were vastus medialis, biceps femoris, tibialis anterior and gastrocnemius medialis. The volunteers walked three times on a straight path in free gait and, further, three times again, but now using the smart walker, to help them with the movements. Then, the data from gait pattern and muscle signals collected by sEMG and accelerometers were analyzed and statistical analyses were applied.
The accelerometry allowed gait phase identification (stance and swing), and sEMG provided information about muscle pattern variations, which were detected in vastus medialis (onset and offset; p = 0.022) and biceps femoris (offset; p = 0.025). Additionally, comparisons between free and walker-assisted gaits showed significant reduction in speed (from 0.45 to 0.30 m/s; p = 0.021) and longer stance phase (from 54.75 to 60.34%; p = 0.008).
Variations in muscle patterns were detected in vastus medialis and biceps femoris during the experiments, besides user speed reduction and longer stance phase when the walker-assisted gait is compared with the free gait.
Keywords Stroke,sEMG,Smart walker,Gait,Accelerometer
Stroke is considered a major health issue worldwide, since it is a leading cause of motor disabilities, affecting the independence and ability to perform daily tasks in most cases (Belda-Lois et al., 2011; World…, 2015). There are two distinct types of stroke: the ischemic and the hemorrhagic. The first one is the most common and is responsible for 85-90% of cases, while the second type occurs in a smaller number (10-15%). In contrast, the mortality rate ranges from 8 to 12% for the ischemic type, while the hemorrhagic type has more fatal outcomes with numbers varying between 33% and 45% (Ovbiagele and Nguyen-Huynh, 2011).
Aside from the stroke type, the location and extension of the brain lesions may lead to different sequels (Deb et al., 2010) and, due to this reason there is a high heterogeneity among stroke sequels (Belda-Lois et al., 2011), varying according to the brain lesion location and extension. A lesion that occurs in the anterior cerebral artery, for example, may cause motor injuries predominantly in the lower extremity of the contralateral side, which interfere in the gait and body balance (Pare and Kahn, 2012).
Patients that had stroke usually have spastic muscles in the quadriceps femoris (vastus medialis, vastus lateralis, vastus intermedius and rectus femoris) and triceps surae (gastrocnemius medialis, gastrocnemius lateralis and soleus) while the hamstrings (biceps femoris, semitendinosus and semimembranosus) and tibialis anterior are flaccid, hindering the knee flexion and dorsiflexion (Murray et al., 2014; Sheffler and Chae, 2015). In spite of flexor weakness, stroke individuals present more co-contractions between agonist and antagonist muscles when compared with healthy subjects (Shao et al., 2009), which occur in order to avoid knee and plantar hyperflexion.
All these conditions create a tendency on stroke individuals to produce a compensatory movement in order to walk, which is known as hip circumduction, typical in stroke gait (Whittle, 2007), causing an asymmetric gait, and overloading the non-paretic limb.
Due to this asymmetry and lack of balance, about 75% of stroke patients need assistance for walking independently during the first three months after stroke onset (Verma et al., 2012). However, there are no evidence-based criteria for choosing the device to help the patient (Verma et al., 2012). Tyson and Rogerson (2009) evaluated the use of cane and foot-ankle orthosis, which provided confidence and safety to the patients (20 stroke patients; mean age: 65.6 ± 10.4 years; mean time since stroke: 6.5 ± 5.7 weeks), improving their functional mobility. On the other hand, Suica et al. (2016) analyzed the immediate effect using a rollator, although for healthy subjects (19 subjects; 22 to 70 years), identifying a reduced muscle activity of the lower limbs (gluteus medius and maximus, rectus femoris, semitendinosus, tibialis anterior and gastrocnemius) caused by the weight bearing imposed on the walker.
Most stroke individuals need rehabilitation, whose main goal is the movement recovery to allow them to carry out daily tasks independently (Dohring and Daly, 2008; Roger et al., 2011). This rehabilitation depends on many factors: lesion severity, age, type of therapeutic intervention, and how complex the stroke was. However, in many cases, rehabilitation does not provide an efficient recovery, and sometimes worsening the clinical status and the damage in the non-paretic limb. In such cases, those therapeutic interventions may provoke decreased mobility and secondary complications (Allen et al., 2011). On the other hand, conventional gait training and rehabilitation, commonly used nowadays, may not provide a total restoration for most patients (Dohring and Daly, 2008; Suica et al., 2016).
Many studies (Cifuentes et al., 2014; Dohring and Daly, 2008; Tan et al., 2013) used robotic devices for motor rehabilitation, to recover important features of the gait and maintain muscle integrity. However, to the extent of our knowledge, no neuromuscular analysis was performed using robotic walkers applied for stroke rehabilitation. The main goal of this paper is to analyze changes in the muscle pattern on paretic limb during free and walker-assisted gaits in stroke individuals, through accelerometry and surface electromyography (sEMG). Another important goal is to verify the volunteer adaptation to a smart walker in the first contact. Therefore, this study is focused on the pattern-variation analysis of the paretic limb muscles and the swing and stance phase duration, in addition to the walking speed during the use of robotic walker and in free gait.
Eight ischemic stroke individuals (4 males and 4 females; 65.75 ± 6.27 years old), from a rehabilitation institution of Espirito Santo state (Brazil), volunteered for the experiments. The number of volunteers generated a sample size for this study that has an effect size of 0.8, with statistical power of 50% and alpha equals 0.05. The research was previously approved by the Ethical Committee of Federal University of Espírito Santo (UFES/Brazil) and all volunteers signed the informed consent.
Eligibility criteria for inclusion in this study were: only one stroke that happened at least from 6 months up to 5 years before the tests; hemiparetic gait; Functional Ambulation Classification – FAC (Holden et al., 1984) in stage 2 or higher; ability to remain erect and with elbows at 90º while using the smart walker; age range from 50 to 80 years; enough cognitive skills and language to follow the experiment instructions. Individuals were excluded if they could not walk independently, had any musculoskeletal or neurological disorder limiting ambulation unrelated to the stroke, and if they had cardiorespiratory impairment, conditions that may prevent them from performing walking tests. Each volunteer was classified through a functional walking test (FAC) by the same physiotherapist, who has more than 20 years of experience.
sEMG and accelerometer data
All procedures for sEMG data acquisition and processing were based on recommendations of the “Standards for reporting EMG data” (Merletti and Torino, 2015). The kind of electrodes used was Ag/AgCl discoid shape, with 10 mm diameter, pre-gelled and with inter-electrode distance of 20 mm. Before the electrode placement, the skin was cleaned (alcohol 70%) and shaved to reduce impedance. Signals from four muscles of lower limb — vastus medialis (VM), biceps femoris (BF), tibialis anterior (TA) and gastrocnemius medialis (GM) — were acquired and analyzed. In addition, a reference electrode was placed on the medial malleolus. In all cases, the analyzed limb was the contralateral to the brain lesion. For better accuracy in electrode placement, two experts checked the electrode position placed on the muscles. Cables from the sEMG acquisition equipment were fixed on the limb using adhesive tape to minimize motion artifacts. In addition, a biaxial accelerometer was fixed using adhesive tape on the ankle of the contralateral limb, with the y-axis pointing cranially and x-axis pointing anteriorly.
Both sEMG and accelerometer data were recorded simultaneously using an acquisition equipment EMG 830C (EMG System do Brasil Ltda®) with 16-bit analog/digital conversion resolution, amplifier gain up to 2000V/V, common mode rejection > 100dB, input impedance of 109Ω, and maximum sampling frequency of 2 kHz. The measurement capacity ranged from -2000 to 2000 μV with sensitivity of 0.061 μV.
A smart walker from UFES/Brazil (Valadão et al., 2016) (Figure 1) was used in the experiments, which was built from a conventional four-legged walker adapted to a robotic mobile platform. The smart walker structure has forearm bars to provide weight support and comfort during its use, also allowing the user to guide it. The smart walker has also a height adjustment, which allows the user to stay in an upright posture. An onboard laser sensor is used to provide information about the distance from the walker to the user’s leg. By using the information provided by the laser sensor, the walker can adjust its speed through a proportional–integral–derivative controller (PID), with the goal of keeping the user at a predefined distance and angle, thus aiding him/her to maintain right posture (position and orientation) while using the device. […]