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16
Lower Limb Analysis of the Biomechanical Gait Cycle at Various Phases in Real Time
Zainab M. Nahi, Huda F. Jameel*, and Ahmed B. Fakhri
Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, AL-Doura, AL-Masfi str., Baghdad, Iraq
*e-mail: huda [email protected]
Abstract. Biotechnology is playing an extremely important part in medical advances. In actuality, they are the basis of pathology diagnosis, which provides doctors with the quantitative data required to choose the best treatment. This study looks at the facts of a case of weak muscular activity and devises solutions to help the disabled or very ill for the specialist to improve the patient's condition by choosing accurate treatment. This would enhance their psychological condition and make it easier for them to do their daily activities. The data are collected by surface electromyography (EMG) from the lower limb of the leg gait events (heel strike, foot flat, midstance, heel off, toe-off, and medium swing) on the right, left, and both legs are estimated. The system consists of a microcontroller, a myograph sensor, and Bluetooth. Healthy individuals utilize both legs regularly in a balanced manner and during a walk as well as stair ascending tests. On both sides of the legs (right and left), sensors are placed on the quadriceps, hamstrings, tibialis, and triceps muscles. The system was tested on 28 people (17 males and 11 females) aged 24-54 years old. The suggested method is used to analyze gait in real-time. © 2023 Journal of Biomedical Photonics & Engineering.
Keywords: bluetooth; gait cycle; hamstrings muscle; myograph sensor; microcontroller; quadriceps muscle; surface emg; tibialis muscle; triceps muscle.
Paper #8682 received 4 Mar 2023; revised manuscript received 14 May 2023; accepted for publication 29 May 2023; published online 13 Jun 2023. doi: 10.18287/JBPE23.09.020304.
1 Introduction
Electromyography (EMG) is a method for measuring and monitoring electrical impulses in muscle contraction. An EMG examination can be used in a variety of clinical and biological circumstances. Surface EMG is employed in experimental gait analysis and for physiological diagnostics. EMG is applied to control prosthetic devices such as artificial limbs, forearms, arms, knees, and lower limbs. So, a systematic review of current publications on the EMG activity of six muscles throughout physical fitness activities in healthy individuals was proposed [1]. However, the level of proof in the included papers was mostly medium, suggesting that further high-quality research was needed to decrease the risk of distortion and make definite conclusions concerning muscle activation. Also, study of electromyography systems, control models, problems, and potential future approaches was introduced [2]. This review focuses on the potential for
EMG control to enable expanded capabilities in mechanical lower limb prostheses as well as the challenges, knowledge gaps, and research possibilities in this area from the viewpoints of patient care and individual muscle control. In addition, to capture the mechanical behaviour of the lower leg, staircase ambulation was used [3] as a basis for wearable technology and control systems. This article would specifically address transitions between level walking and stair mobility, as well as transitions between bipedal walking and walking.
Biomedical technologies are becoming increasingly important in modern medicine. In reality, they were at the heart of pathology diagnosis and follow-up, providing doctors with the statistical data needed to choose the best medication. The biomedical techniques that are used in gait parameters, covering the top issues utilized in clinical practice for neurologic, musculoskeletal, and rheumatic disorders and emphasizing their usefulness in
the clinical environment [4]. In addition, electrical impedance myography (EIM) is a bioimpedance method that records the voltage produced by delivering a low or high over a muscle area without triggering myofiber or neuronal nerve impulses. The volume conduction properties (VCPs) of the muscle were measured using EIM. VCPs have the potential to be used as a standardized, measurable, and reliable electrophysiological diagnostic of muscle [5]. A classification tree gait pattern identification model effectively detected loading response, push-off, and swing for five distinct environments as well as a variety of walking speeds, using input data from the leg and knee was proposed [6]. The classifier proved resistant to a variety of simulated travel scenarios encountered in daily life and was able to generalize previously unknown data from diverse participants. Furthermore, displaying muscle activation and strength utilizing an EMG with auditory, vision, and/or motion presentation has been accomplished [7]. However, employing conductive gel to link the EMG electrode to the skin surface would generate a steady signal of muscle activity
There are many areas, which can measure the muscle activity, and there are many researches in these fields such as to examine the worker's body position during lifting and to compare the use of lower back support while lifting. It also uses EMG to study the worker's muscle function during excessive lifting and CATIA Software to replicate the worker's movement for rapid upper limb assessment (RULA) findings. The results demonstrate that workers who use lower back support have raw EMG signals with lower muscle activity than workers who do not use lower back support. When compared to not using back support, utilizing belt support can lower muscular activity by up to 67.4% [8]. Similarly, development of low back discomfort (LBD) when standing was linked to spinal muscle activation and pelvic motion. In analysis, the system looked at whether age, gender, and long-term work habits modified the LBD. However, given the relatively minor changes discovered, the therapeutic significance of this discovery in the chosen difference requires further investigation [9]. Although, a surface electromyography (sEMG) electrodes were used to measure muscle activity in the bilateral and trapezius muscles during the continuous farmland preparation procedure. Objective preference findings suggest that the bilateral, tracked by the trapezius left muscle, were the most uncomfortable during earthwork [10]. Based on machine learning, a unique approach for classifying gait phases in power gait exoskeleton users was proposed [11]. A period camera mounted on the crutches used for assisted walking was employed to capture the depth information. The machine learning approach predicts the first stage of data collection and processing throughout which the 3D points related to the foot and those related to the surface are established.
On the other hand, there are many research measured the muscle activity for arm, forearm, upper arm, and wrist as well as arm like an electrical device that can estimate
the activity of any muscle in the human body was generate. The contraction in the normal case, with athletic body muscles, as well as in the situation of neuromuscular disorder was designed [12]. The initial concept was based on a biosensor on the biceps muscle. Muscle activity was monitored using different hand-loaded weights and varied ranges of action of the elbow joint. In addition, a surface EMG signal based joint torque estimate technique was provided to quantify the motion intention. To improve the accuracy of the calculated torque, a neural network was employed to learn the ideal factor of muscular strength. However, it should focus on creating a control technique based on this torque estimation method to operate rehabilitative exoskeleton robots [13]. Moreover, examines the effectiveness of stroke survivors in terms of detecting weak muscle activity and reaction by involuntary amplitude variation of EMG signals was illustrated. Using surface EMG data obtained from six hemiparesis stroke patients and six healthy participants, according to the findings, the amplitude-independent algorithm performed better in terms of recognizing weaker muscle activity and avoiding false alarms. However, this may be done by utilizing surface EMG data from individuals with various kinds of damage to validate the effectiveness of amplitude-independent approaches for detecting muscle activation [14]. Besides, a compare EMG for handstands done on three different pieces of equipment (ground, circles, and parallel bars), as well as the variation between juvenile and highly-trained senior gymnasts was presented. The study included ten adults and fifteen young gymnasts. Researchers looked at the magnitude of EMG signals in thirteen muscles that were standardized by the arbitrary angle of maximal exercise voluntary movement [15].
A new force myography (FMG) sensor has been developed to identify physical muscle contractions in lost limbs [16]. FMG is an approach for registering limb muscle activity utilizing accelerometer sensors and movement observation applications. Notwithstanding its recent recognition among investigators, many of its essential properties are right now unclear. The FMG consists of one or maybe more sensors for obtaining muscle contraction data from missing limbs and an embedded system that receives input data from the sensors as well as produces control instructions. Also, techniques for converting FMG signals from the sensing element to control instructions have been developed. Moreover, a muscular activity visualization system was developed to investigate the mapping link between hand gestures and activated upper arm-specific muscles. The graphics system's human-computer interaction showed that this approach could visually depict the relationship between multiple channels in the image space, allowing users to intuitively recognize the activity intensity of different muscles in hand movements [17]. To determine the lowest sample frequency required for capturing upper-limb FMG responses without compromising signal quality. Twelve participants took part in the test in which they were taught to perform quick hand motions while
FMG responses from the wrist and the bulk area of the arm were monitored [18].
In the same way, a muscle activity for the lower limbs and knee is clarified like postural control, as well as trunk independence, increased considerably after multi-muscle stimulation of the lower limbs was proposed. The aforementioned lower body part multi-muscle stimulator training strategy, using standard parameters at submaximal rates, is therefore expected to benefit trunk functional status. To test the generalizability of the findings and the motor patterns in assisted stepping, the number of musculoskeletal electrical events, stand training routines, and a larger sample size of studies were required [19]. Along with, to examine the effects of hippotherapy on lower-body activation and gross motor performance in participants with cerebral palsy in a population with appropriate motor development. It is possible to infer that hippotherapy offered a sequence of muscular stimulation that resulted in gains in capability and lower limb activation, and this treatment practice could contribute to the active recovery and better motor ability of people with cerebral palsy [20]. Four short pieces of training of submaximal attempt in eccentric (ECC) cycling resulted in lower activities of the rectus femoris (RF), biceps femoris (BF), soleus (SOL), and vastus lateralis (VL) muscles. Also, an impression of mostly less effort in ECC than in Concentric (CON) cycling across a variety of power outputs - despite the fact that most different factors were not different between exercise methodologies prior to the event. Subdivisions in muscular activity and heart rate decreased following the cycling events but were generally larger with ECC cycling. However, before doing any muscular activity or sensory tests, future studies should consider undertaking numerous periods of ECC cycling [21]. In spite of, to examine hip and knee muscular activation during single-limb standing and single-limb squat in men having patellofemoral pain (PFP) as well as a control group, which thus did not have PFP was proposed. Furthermore, muscle activation patterns differed between the PFP and healthy individuals. As a result, the findings confirm the idea that any impairment in the intermediate muscles involved in the leg might have an impact on the patellofemoral joint [22].
Inertial motion detectors are essential components of today's intelligent devices. They have the ability to be a component of future technologies like intelligent buildings, the internet of things (IoT), and the internet of everything (IoE). A two-stage sensor fusion technique that produces joint angle in real-time was proposed, implemented, and verified [23]. In the first stage, the drifts in each inertial sensor combined gyroscope data were estimated using the method designated gradient declivity. To provide a real-time evaluation of the angular position, the inclination data from both inertial sensors were combined in the second stage using a gradient declivity approach. Examines and develops a mobile gait detection method for patients depending on an inertial measurement unit, including the hardware and software design of the data collection module, were
presented [24]. Control performance of the musculoskeletal systems is used to determine gait kinematics and reaction mechanisms [25]. Inertial sensor information proposes a methodology for immediately estimating gait from inertial sensors using a single dynamic algorithm. Furthermore, the muscular effort was reduced to ensure a one-of-a-kind response. A seven-inertial measurement was used to collect data from 10 people walking and jogging at six different speeds. Create practical and reliable approaches for detecting gait movements in people with gait abnormalities in realtime. In addition, a collection of methods for online identification of gait patterns and actions was presented. Furthermore, they created an upgraded version of an IMU-based portable device that enables gait data monitoring and recording [26].
Typical gait analysis approaches were carried out under controlled conditions in a costly motion-capture laboratory. Although the technique produces reliable findings, it does not allow for continual monitoring and evaluation. Wearable systems with inertial measurement units are an option. However, inertial sensor suffers from drift as a result of the double processing used to obtain position information. However, ultrasonic sensors can be used to counteract this drift. A simple, reliable measure of the ankle angle (AA) utilizing ultrasonic sensors and triangulation methods without using sophisticated inertial sensor techniques was proposed [27]. Furthermore, the suggested technique detects the minimal toe clearance (MTC), which was another significant gait parameter. The suggested system analyzes a person's gait for long durations for a day scenario and assesses essential gait characteristics that were utilized for gait problem therapy and fall detection in the older adult population. Finally, a novel approach for measuring knee joint angle was proposed. The technique used force myogram responses from thigh muscles as individuals walked on a machine at various speeds, including slow, moderate, high, and run. The method has been validated on both able-bodied people and a transfemoral prosthesis. However, research might look at expanding the current technique to a more diverse patient group [28]. The actions of the multifidus, internal oblique, quadriceps, and tibialis anterior muscles in inactive adults were identical to those observed with walking and hippotherapy employing various methods and elements. The results of this controlled research may be compared to those of patients with various clinical illnesses who appear to have independent walking impairment in order to truly comprehend the alterations that happen in their trunks [29].
The drawback from the previous works is that they have used only one sensor location and one to five muscle types, so to get effective results in collecting data and analyzing it, this paper uses heel-strike, foot flat, midstance, heel-off, toe-off, and medium swing of the right leg, left leg, and both legs. This paper has been classified several related studies based on sensor location, sensor type, muscle test, and gender and
compared with proposed method as illustrated in (Table 1).
The purpose of this study is to get a more thorough understanding of the challenges posed by weak muscular activity and to propose options to assist the disabled by enabling the specialist to assist patients, improve their situation and stimulate the weak muscles. The aforementioned would improve their psychological state and enable them to do their everyday tasks more naturally. The system components include a Bluetooth module, a myograph sensor, and a microcontroller. It was established during a walking and stair climbing test that healthy participants use both legs equally and in a balanced way.
The research contributes:
1. A review of the use of muscle activity systems in previous works.
2. To collect surface EMG data from the lower limb of the leg.
3. To predict gait events (heel-strike, foot flat, midstance, heel off, toe-off, and Medium swing) of the right leg, left leg, and both legs.
2 Methodology
The suggested system consists of a myograph put on the limb of the user, the data (muscle activity) is wirelessly transmissible to a computer or phone for analysis, and a microcontroller processes the data. The sensors were placed on the legs of the chosen muscles, namely quadriceps, hamstrings, tibialis, and triceps on both sides (right and left). The suggested method can be used for real-time motion analysis. The system is simple to wear, practical for outdoor usage, lightweight, and commercially feasible. Fig. 1 represents the block diagram of the system with all components.
Table 1 Comparison of sensor location, sensor type, muscle test, and gender in previous works.
Year, Ref.
Sensor location
Sensor
type
Muscle test
Gender
2020, [8]
back
myograph
low back
7 male
2020, [12] arm myograph biceps 4 persons
2021, [13] arm myograph elbow joint angle not available
2020, [14] arm myograph not available 6 healthy and 6 hand impairment (4 female)
2019, [19] lower limbs myograph
2019, [9] back myograph erector spinae and medio-lateral pelvic 15 young females, 15 young males, 15 older males, and 15 young males
2019, [20] lower limbs myograph 7 individuals
2019, [22] hip and knee muscles myograph not available 18 males
2020, [16] forearm force myography sensor forearm 1 male
2020, [17] arm sEMG upper arm muscle not available
2019, [15] arm EMG anterior deltoid, pectoralis major, latissimus dorsi, biceps brachii, and trapezius descendent muscles 10 adult and 15 young
2021, [10] bellies sEMG trapezius and biceps brachii 10 volunteers
2019, [18] wrist and forearm force myography (FMG) not available 12 volunteers
2020, [28] knee FMG thigh muscles 8 men
Proposed method lower limbs myograph quadriceps, hamstrings, tibialis, and triceps on both sides (right and left). 28 persons (17 males and 11 females)
Microcontroller
Bluetooth
Phone
Computer
Fig. 1 Block diagram of the system.
Switc
Bluetooth
5V DC
Sensors
Microcontroller Fig. 2 Schematic and the hardware components of the system.
Table 2 Action of muscles during activities.
Muscle name Action Ph. 1 Ph. 2 Ph. 3 Ph. 4 Ph. 5 Ph. 6
Quadriceps knee extensors helping in control of knee flexion W. W. N.W. W. N.W. N.W.
Gluteus Maximus Move of the hip and thigh W. W. N.W. N.W. N.W. N.W.
Hamstrings Extend the hip and flex knee via concentric contraction W. N.W. N.W. N.W. N.W. W.
Iliopsoas hip flexor and active during the initial and mid-swing phase N.W. N.W. N.W. W. W. N.W.
Tibialis Anterio During loading response and initial swing and the ankle dorsiflexion during the late swing phase. W. W. N.W. W. W. W.
Triceps During late mid-stance and terminal stance N.W. N.W. W. N.W. N.W. N.W.
Ph.: phase, W.: work, N.W.: not work,
Ph. 1: heel strike, Ph. 2: foot flat, Ph. 3: Midstance, Ph. 4: Heel off, Ph. 5: Toe release, Ph. 6: Medium swing.
Fig. 3 System flowchart.
Besides, Fig. 2 illustrate the schematic and the hardware components of the system, which comprises an Arduino Pro Mini board [30, 31], two MyoWare muscle sensor module [32], and Bluetooth [33, 34]. The signals from the MyoWare are received by the microcontroller, and the computer or phone serves as an adapter between the microcontroller and the MyoWare. In the Arduino programming, the obtained signals are processed using a specified algorithm that cleared as a flowchart which is depicted in Fig. 3.
3 Data Collection
The position and origin point of the muscles themselves determine the individual body parts responses to a muscle function. To make this procedure simpler, the system will concentrate on the body's skeletal muscles. The mobility of the majority of skeletal muscles is restricted or guided by their attachment to bone, cartilage, or connective tissue. The testing of the data is applied to 28 normal people (17 males and 11 females) aged from 24 to 54 years old. The data for this work has been collected from following:
Part 1: for normal walking with and without weight (5 kg, 10 kg, and 15 kg); for quadriceps as well as hamstring muscles, once for left leg and once for right leg with six phases.
Part 2: for stair clamping with and without weight (5 kg, 10 kg, and 15 kg), for quadriceps as well as hamstring muscles, once for the left leg and once for the right leg, with six phases.
Part 3: six phases for two legs at the same time for normal walking and stair clamping with and without weight (5 kg, 10 kg, and 15 kg) for quadriceps and hamstring muscles.
Part 4: for normal walking with and without weight (5 kg, 10 kg, and 15 kg); for Tibialis and triceps muscles, with six phases, once for left leg and once for right leg.
Part5: for stair clamping with and without weight (5 kg, 10 kg, and 15 kg), for tibialis as well as triceps muscles, once for the left leg and once for the right leg, with six phases.
Part 6: six phases for two legs at the same time for normal walking and stair clamping with and without weight (5 kg, 10 kg, and 15 kg) for Tibialis and triceps muscles.
4 Result and Discussion
For muscle evaluation by observing the user and recording the readings of muscle contraction and relaxation during walking and climbing the stairs for all the parts that were mentioned in the collection of data, it is notice that some muscles strengthen and weaken during the phase that is assigned to them. As illustrated in Table 2, that shows the action of muscles during activities.
The collected data presented in Table 3 represented the quadriceps and hamstring muscles and the sensor on the left leg (with or without weight). The numbers in the tables are the activity of the muscles as a voltage that was collected from the sensor, which is measured in microvolts, and Fig. 4 shows the phases for each case.
The collected data presented in Table 4 represented the quadriceps and hamstring muscles and the sensor on the left leg (with or without weight). The numbers in the tables are the activity of the muscles as a voltage that was collected from the sensor, which is measured in microvolts, and Fig. 5 shows the phases for each case.
The collected data presented in Table 5 represented the tibialis and triceps muscles and the sensor on the left leg (with or without weight). The numbers in the tables are the activity of the muscles as a voltage that was collected from the sensor, which is measured in microvolts, and Fig. 6 shows the phases for each case.
Table 3 Activity of the quadriceps and hamstring muscles in microvolts (with or without weight).
Heel strike Foot flat Midstance Heel release . Medium swing
name release
Gait cycle with normal walk
Quadriceps 251 212 198 279 328 231
Hamstrings 92 70 56 91 88 57
Normal walk with 5 kg in the left hand
Quadriceps 88 152 244 221 243 191
Hamstrings 66 171 234 342 355 244
Normal walk with 10 kg left hand
Quadriceps 297 236 145 163 255 205
Hamstrings 73 87 87 26 65 60
Normal walk with 15 kg left hand
Quadriceps 317 329 300 314 334 330
Hamstrings 499 455 416 433 412 366
Normal walk with 5 kg in the right hand
Quadriceps 323 327 322 312 355 368
Hamstrings 315 312 300 307 355 352
Normal walk with 10 kg right hand
Quadriceps 362 312 308 323 330 333
Hamstrings 448 318 308 397 319 324
Normal walk with 15 kg right hand
Quadriceps 170 178 129 186 215 134
Hamstrings 174 192 132 152 150 139
b
a
c
Table 4 Activity of the quadriceps and hamstring muscles in microvolts (with or without weight).
Heel
Muscle name ^ ., Foot flat Midstance
Heel release
Toe release
Medium swing
Stair climbing
Quadriceps 116 224 214 128 146 139
Hamstrings 227 325 238 231 265 390
Stair climbing with 5kg in the left hand
Quadriceps 195 213 215 171 214 153
Hamstrings 255 291 282 263 253 276
Stair climbing with 10kg left hand
Quadriceps 151 212 165 191 245 303
Hamstrings 256 281 216 254 291 250
Stair climbing walk with 15kg left hand
Quadriceps 128 219 231 280 192 244
Hamstrings 312 236 314 285 218 234
Stair climbing with 5kg in the right hand
Quadriceps 125 237 195 324 142 205
Hamstrings 157 218 253 112 214 176
Stair climbing with 10kg right hand
Quadriceps 163 512 378 114 221 251
Hamstrings 193 318 200 134 161 250
Stair climbing walk with 15kg right hand
Quadriceps 192 352 590 408 400 619
Hamstrings
183
272
380
357
215
305
Fig. 5 Phases of a) foot flat phase, b) toe release phase, c) medium swing phase, d) midstance phase, e) midstance phase, f) heel strike phase.
Table 5 Activity of the tibialis and triceps muscles in microvolts (with or without weight).
Muscle name ., Foot flat Midstance Heel release Toe release Medium swing
Stair climbing
Tibialis 121 221 349 135 128 166
Triceps 94 124 149 121 139 96
Stair climbing with 5 kg in the left hand
Tibialis 74 225 357 169 134 186
Triceps 98 130 176 132 175 114
Stair climbing with 10 kg left hand
Tibialis 212 214 394 124 169 102
Triceps 105 102 174 175 168 87
Stair climbing walk with 15kg left hand
Tibialis 137 246 225 235 195 285
Triceps 114 113 224 318 405 163
Stair climbing with 5kg in the right hand
Tibialis 266 272 379 228 199 168
Triceps 120 147 173 182 291 158
Stair climbing with 10kg right hand
Tibialis 121 276 527 287 282 202
Triceps 106 147 183 126 389 246
Stair climbing walk with 15kg right hand
Tibialis 145 445 286 338 214 209
Triceps 109 122 195 210 187 124
Fig. 6 Phases of a) foot flat phase, b) medium swing phase.
The collected data presented in Table 6 represented the right (R.) triceps and left (L.) triceps muscles and the sensor on the left and right leg (with or without weight). The numbers in the tables are the activity of the muscles
as a voltage that was collected from the sensor, which is measured in microvolts, and Fig. 7 shows the phases for each case.
Table 6 Activity of the right (R.) triceps and left (L.) triceps muscles in microvolts (with or without weight).
Muscle name . .. Foot flat Midstance Heel release Toe release Medium swing
strike
Normal walk
R. Triceps 138 141 193 124 120 117
L. Triceps 130 124 127 103 200 190
Normal walk with 5kg in the left hand
R. Triceps 85 202 145 76 162 188
L. Triceps 167 111 103 105 231 193
Normal walk with 10kg left hand
R. Triceps 124 80 177 70 160 190
L. Triceps 118 140 121 155 181 204
Normal walk with 15kg left hand
R. Triceps 88 374 356 98 213 188
L. Triceps 125 160 109 100 165 211
Normal walk with 5kg in the right hand
R. Triceps 92 208 292 100 122 128
L. Triceps 128 193 124 105 212 231
Normal walk with 10kg right hand
R. Triceps 106 190 324 80 79 115
L. Triceps 130 163 146 107 228 282
Normal walk with 15kg right hand
R. Triceps 167 140 355 86 135 127
L. Triceps 110 240 110 100 244 258
Fig. 7 Phases of a) heel strike phase, b) Midstance phase.
From the results, in normal walking with quadriceps muscles, when the weight is in the left hand, the activity increases, and in stair climbing, when the weight is in the left hand, the activity decreases significantly in the first three stages and increases in the other three. Also, in normal walking with tibialis muscle weight in the left hand, the activity increases, and when stair ascending with weight in the left hand, the action is the same as with weight. In addition, in a comparison between the left and right triceps muscles, when the weight is in the left hand, the activity increases in the left muscle more than the right, and when the weight is in the right hand, the activity increases in the right muscle more than the left.
5 Conclusion
In this paper, the activation values of the tibialis, triceps, quadriceps, and hamstring muscles are measured and analyzed in different conditions. The value is found by a wireless system using an Arduino microcontroller and wearable muscle sensors. With the practical experience, which involved the gait cycle at various phases, the biomechanics influence was implemented. On the right, left, and both legs, gait cycles are computed in real-time
for heel contact, foot neutrality, oriented practices, heel release, toe-off, and medium swing. The results are found with phases of the gait cycle in normal walking and upstairs, with and without lifting weight. The measurements are applied to 28 individuals (17 males and 11 females) aged 24-54 years old, and the collected data represents the voltage activity of the muscles that can be used for future work to get more equate results to analyze the biomechanical gait cycle at various phases in real time.
Acknowledgements
For their assistance throughout this investigation, the authors gratefully acknowledge the Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University.
Disclosures
The authors have no relevant financial interest in this article and no conflict of interest to disclose.
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