Effect of wearable robot Bot Fit’s hip joint-centered assist torque and voice coach on walking

doi:10.21203/rs.3.rs-4714708/v1

Download PDF

Research Article

Effect of wearable robot Bot Fit’s hip joint-centered assist torque and voice coach on walking

https://doi.org/10.21203/rs.3.rs-4714708/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

The main key to the 4th industrial era is robots, and wearable robots are incorporated into human healthcare. Samsung Electronics' Bot Fit is a hip joint-centered assistive robot that can induce walking posture and energetic walking exercises.

Methods

This study is a cross-section study. Fifty-eight subjects consisting of older and younger adults participated. The straight walking test was conducted under the conditions of bare body, wearing the wearable robot Bot Fit assist mode, and applying voice coach. Spatio-temporal gait parameters were analyzed and the statistical significance level was set at 0.05.

Results

When assist mode and voice coach were applied, pelvic movement in 3 axes, stride length, walking speed, and swing phase ratio compared to the bare body increased, and in assist mode, the difference in left and right stride lengths decreased.

Conclusion

The appropriate combination of assist mode and voice coach can increase the effectiveness of walking exercise while wearing Bot Fit.

Trial registration

Clinical Research Information Service, KCT0007974. Registered 12/07/2022

Robotics

Gait

Posture

Pelvis

Movement

One of the keywords in the 4th industrial era is the robot business. In particular, in the robot business, along with recent interest in healthcare, healthcare robots which help human’s life are being actively researched^1–3. According to recent studies, various types of robots are appearing for healthcare. Among them, unlike other type of robots, wearable robots are characterized by close interaction with human life^4,5. Various types of wearable robots are being developed, and among them, robots that assist walking centered on the hip joint of the lower extremities are appearing^6,7. Research results are showing that when assistance is provided centering on the hip joint, there is a positive effect in terms of increased energy efficiency and posture during walking¹. In the case of the robot Bot Fit, developed by Samsung Electronics, the flexion and extension of the hip joint is assisted through five intensity of torque. It has been reported that walking using this assist torque increases speed or stride length⁸.

Another important element of walking is the movement of the pelvis. In general, a pelvic rotation movement occurs in the transverse plane when a human walk at a speed of 0.84 km/h or higher. In addition, natural movement of the pelvis occurs in other two planes, including obliquity in the frontal plane, tilt in the sagittal plane⁹. These movements of tilt and obliquity eventually reduce the movement of center of mass (COM) during human walking, decreasing body sway, increasing energy efficiency during walking, leading to stable walking¹⁰. The pelvis is positioned in the middle connecting the lower limbs and upper limbs. Due to this positional reason, when the lower extremity extends forward for walking, the pelvis reacts through the movement in the three planes and absorbs the shock of the spine connected to the upper part^10,11. In addition, since the pelvis moves in the opposite direction to the direction of the legs, it creates an arm swing in the direction of crossing the upper limbs to prevent the body from leaning to one side during walking¹².

Bot Fit has been studied on spatio-temporal parameters such as speed, stride length in previous studies^1,6. It is necessary to check how these wearable robots affect not only spatio-temporal parameters but also one of the kinematic parameters, the movement of the pelvis. When the movement of the pelvis is naturally increased, besides the fact that the wearable robot simply increases the speed, it can be confirmed by what mechanism the speed is increased. It is also possible to confirm whether a more Bot Fit can induce a functional gait, and to predict if it affects the movement of the pelvis, it can also affect the core muscles which move the pelvis. In addition, the symmetry of the stride length of both legs is an important factor in improving the quality of walking¹³. If the gait with poor symmetry continues, activity of various muscles can negatively affect the correct postural alignment¹⁴, and the gait with an unbalanced posture can reduce the energy efficiency of walking. In the Bot Fit, two actuators located on user’s both greater trochanters assist hip flexion and extension with the same torque. Due to this mechanism, it is necessary to check how symmetrically the size of the user’s stride on both side changes compared to when the Bot Fit is not worn.

Voice coach is one of several feedback methods that have been used before during exercise, and unlike other feedback methods, it has the advantage of being able to accurately deliver what is to be instructed. Voice coach affects the judgment of the subject's brain and leads to immediate and voluntary performance according to that judgment¹⁵. In the case of walking, controlling the speed or stride length that can be performed at will is expected to have a significant impact on voice coach. In this way, walking exercise using assist mode or voice coach is thought to be able to increase the effectiveness of walking exercise from a long-term perspective. Therefore, this study was designed to confirm the immediate effect of Bot Fit's assist torque and voice coach function while walking.

Participants

This research is an exploratory study conducted using a cross-sectional design and the findings are presented following the recommendations specified in the STROBE statement¹⁶. This study was conducted from November to December 2022. A convenience sample of thirty healthy young adults aged between 20 and 40 years were recruited from the staff and student population of the Sahmyook University. Additionally, thirty participants aged 65 or older were recruited from the community.

All were free of a history of musculoskeletal disorders, neurological pathology, orthopedic conditions, and cardiovascular conditions. Participants with impaired cognitive function (i.e., Mini-mental State Exam or MMSE score < 25) were excluded. The study also had particular exclusion criteria for the use of the Bot Fit that excluded individuals who did not meet certain height or weight criteria suitable for the device. After being provided with written information about the experimental procedures, the participants in the study gave their consent by completing a written consent form. Then, data on participants' demographics (such as sex and age) and anthropometric measurements, including height, weight, and body mass index (BMI), were collected. This study was approved by the Institutional Review Board of Sahmyook University (IRB No. SYU 2022-10-009) and registered by Clinical Research Information Service (KCT0007974).

Wearable robot

The Bot Fit (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) is a wearable robot designed to provide assistive torque and resistive torque for hip movement. It is worn around the waist and thighs and weighs approximately 2.9kg. The Bot Fit includes two actuators, a control pack, power switch, rechargeable battery, thigh support frame, thigh support strap, and waist belt. Each actuator consists of a motor, angular position sensor, and controller. The control pack contains a central processor, inertial measurement unit (IMU) sensor, and rechargeable battery. The actuators generate assistive and resistive torque, which is transmitted to the user's thigh through the thigh support frame. In this study, only the assist mode, which provides assistive torque, was used. When a mobile device is connected to the Bot Fit via Bluetooth, it can adjust the intensity of the assist mode within a scale of 1 to 5. The study utilized intensities of 1 (low-intensity assistance), 3 (moderate-intensity assistance), and 5 (high-intensity assistance).

(Fig. 1. Bot Fit)

Experimental Procedures

Our experiments were carried out in extended corridors exceeding a length of 20-meters. All participants, including both young adults (YA) and older adults (OA), performed two 20-meter walks for each of the 10 different walking conditions (Table 1), following the exact same sequence. To collect data on gait kinematic and spatio-temporal parameters, the G-walk sensor system (BTS G-Walk 2, BTS Bioengineering, Korea) was worn with a waist belt at the sacral vertebra level 1–2 (S1-2) of the participants in all conditions^17,18.

Table 1

Overview of experimental conditions.
Condition	Specification	Equipment
A. Walking without Bot Fit
(1) Without Bot Fit and Voice coach (“Natural”)	Walking naturally without voice coach	G-Walk
(2) With Voice Coach 1 (“VC1”)	Walking with a voice coach 1	G-Walk, Bone conduction hearing aid
(3) With Voice Coach 2 (“VC2”)	Walking with a voice coach 2	G-Walk, Bone conduction hearing aid
(4) With Voice Coach 3 (“VC3”)	Walking with a voice coach 3	G-Walk, Bone conduction hearing aid
B. Walking with Bot Fit
(5) With Bot Fit assist mode 1 (“A1”)	Walking without a voice coach	G-Walk, Bot Fit
(6) With Bot Fit assist mode 3 (“A3”)	Walking without a voice coach	G-Walk, Bot Fit
(7) With Bot Fit assist mode 5 (“A5”)	Walking without a voice coach	G-Walk, Bot Fit
(8) A1 + VC1 (“Combined1”)	Walking with a voice coach 1	G-Walk, Bot Fit, Bone conduction hearing aid
(9) A3 + VC2 (“Combined3”)	Walking with a voice coach 2	G-Walk, Bot Fit, Bone conduction hearing aid
(10) A5 + VC3 (“Combined5”)	Walking with a voice coach 3	G-Walk, Bot Fit, Bone conduction hearing aid

Table 2. Participants Characteristics
Variables	YA (n=30)	OA (n=28)
Sex (Male, Female)	15, 15	12, 16
Age (y)	34.00 (9.11)	77.79 (4.76)
Height (cm)	169.90 (9.89)	156.54 (8.32)
Weight (kg)	70.79 (17.55)	61.91 (8.42)
Body Mass Index (kg/m²⁾	24.52	25.26
Abbreviations: YD: Young Adults, OA: Older Adults, Values are presented as mean (Standard deviation)

Table 3. Gait kinematic and spatio-temporal parameters in Bot Fit assist mode
Variables		W/O A	W A1	W A3	W A5
YA	T R (°)	5.54 (1.88)	6.25^** (1.92)	6.82^** (2.00)	7.23^** (2.47)
	O R (°)	10.11 (2.46)	10.61 (2.42)	11.22^** (2.71)	11.36^** (2.55)
	R R (°)	17.35 (6.53)	20.35^** (7.30)	21.92^** (7.82)	22.94^** (2.55)
	SLD (cm)	3.35 (3.49)	1.62^** (1.55)	2.60 (3.06)	2.55 (2.89)
	GS (m/s)	1.27 (0.11)	1.33^** (0.10)	1.35^* (0.13)	1.37^** (0.14)
	SL (m)	0.66 (0.06)	0.69^** (0.06)	0.7^** (0.07)	0.70^** (0.07)
	SPR (%)	40.03 (1.61)	40.3 (1.37)	40.6 (1.42)	40.53^* (1.28)
OA	T R (°)	5.58 (1.65)	6.20^** (1.55)	6.80^** (1.98)	6.72^** (1.85)
	O R (°)	8.88 (2.53)	10.68^** (3.24)	11.12^** (3.09)	11.10^** (2.97)
	R R (°)	14.63 (4.08)	19.83^** (6.41)	21.45^** (7.17)	21.65^** (6.92)
	SLD (cm)	3.78 (4.76)	3.69 (3.15)	3.40 (3.56)	3.51 (3.06)
	GS (m/s)	1.16 (0.17)	1.27^** (0.19)	1.24^** (0.20)	1.26^** (0.20)
	SL (m)	0.59 (0.06)	0.65^** (0.07)	0.64^** (0.07)	0.65^** (0.07)
	SPR (%)	39.23 (1.25)	39.46 (1.32)	39.80 (1.52)	39.81 (1.60)
Abbreviations: YD: Young Adults, OA: Older Adults, W/O A: Without Bot Fit, W A1: With Bot Fit assist mode 1, W A3: With Bot Fit assist mode 3, W A5: With Bot Fit assist mode 5, T R: Tilt Range, O R: Obliquity Range, R R: Rotation range, SLD: Step Length Differential, GS: Gait Speed, SL: Step Length, SPR: Swing Phase Ratio, p<0.05: significant difference compared to W/O A, *p<0.01 significant difference compared to W/O A. Values are presented as mean (Standard deviation)

Table 4. Gait kinematic and spatio-temporal parameters in voice coach
Variables		W/O A	W VC1	W VC2	W VC3
YD	T R (°)	5.54 (1.88)	7.09 (5.88)	6.61^** (2.07)	7.00^** (2.10)
	O R (°)	10.11 (2.46)	11.19^* (2.81)	11.74^** (2.83)	12.42^** (3.21)
	R R (°)	17.35 (6.53)	28.81^** (9.46)	29.03^** (8.67)	33.47^** (3.21)
	SLD (cm)	3.35 (3.49)	1.73^* (1.66)	2.80 (1.88)	3.15 (3.00)
	GS (m/s)	1.27 (0.11)	1.40^** (0.12)	1.42^** (0.12)	1.53^** (0.13)
	SL (m)	0.66 (0.06)	0.77^** (0.07)	0.77^** (0.08)	0.85^** (0.09)
	SPR (%)	40.03 (1.61)	41.08^* (1.33)	41.01^** (1.45)	41.38^** (1.26)
OD	T R (°)	5.58 (1.65)	6.66^** (1.93)	6.57^** (1.76)	7.24^** (2.22)
	O R (°)	8.88 (2.53)	11.49^** (3.89)	11.7^** (3.64)	12.9^** (3.50)
	R R (°)	14.63 (4.08)	24.39^** (7.73)	25.13^** (7.49)	29.73^** (8.79)
	SLD (cm)	3.78 (4.76)	5.89 (3.39)	3.90 (4.18)	3.67 (3.00)
	GS (m/s)	1.16 (0.17)	1.31^** (0.25)	1.35^** (0.19)	1.41^** (0.22)
	SL (m)	0.59 (0.06)	0.69^**(0.11)	0.69^** (0.08)	0.72^** (0.09)
	SPR (%)	39.23 (1.25)	39.58 (1.53)	39.58 (1.45)	39.77 (1.78)
Abbreviations: YD: Young Adults, OA: Older Adults, W/O A: Without Bot Fit, W VC1: With Voice Coach 1, W VC2: With Voice Coach 2, W VC3: With Voice Coach 3, T R: Tilt Range, O R: Obliquity Range, R R: Rotation range, SLD: Step Length Differential, GS: Gait Speed, SL: Step Length, SPR: Swing Phase Ratio, p < 0.05: significant difference compared to W/O A, *p < 0.01 significant difference compared to W/O A. Values are presented as mean (Standard deviation)

(Table 1)

Voice coach was implemented using bone conduction hearing aid (BE-BONE7, Britz, Republic of Korea) and it comprises a set of three pre-recorded phrases:

1. Voice coach 1 phrase: Lengthen your stride slightly.
2. Voice coach 2 phrase: Lengthen your stride.
3. Voice coach 3 phrase: Lengthen your stride as much as possible.

During walking, the voice coach was provided every three seconds at regular intervals.

Following the provision of the necessary equipment for each condition, participants were asked to take up the start position by standing upright at the starting line. Next, participants were instructed to walk at their usual pace towards the 12 ft instrumented walkway (GAITRite ® platinum, CIR systems, Havertown, PA, USA) located at the end of the 20-meter distance, stopping when they reached the target line (Fig. 2).

The distance of 20 meters was divided into different sections for data stability and analysis purposes. To eliminate instability caused by Bot Fit's control transfer operation, the first 6 meters from the starting point were chosen arbitrarily to be excluded. Additionally, a 2-meter section just before reaching GAITRite was designated as an acceleration zone, while another 2-meter section from the end of the 20 meters, including GAITRite, was defined as a deceleration zone. For data analysis, GAITRite's 10-meter section, excluding the acceleration and deceleration areas, was utilized.

(Fig. 2. Outline of the 20-meter distance)

Data analysis

For the data analysis, we considered the mean of the two trials. The gait kinematic and spatio-temporal parameters were evaluated using the G-Walk. Gate kinematic parameter were pelvic angle (degrees) such as rotation, tilt and obliquity range, gait speed (meter/seconds), stride length (meters), swing phase ratio (% gait cycle). Additionally, step length differential parameter was calculated using the following calculation formula: |(Right step length – Left step length)|

Sample size

To calculate the sample sizes, G*Power software (version 3.1.9.2) was utilized. The analysis involved calculating the difference between two dependent means (matched pairs). The significance level was set at 0.05 for a two-tailed test, and an effect size of 0.5 was assumed. Additionally, a power of 0.80 was desired to detect meaningful effects. As a result, a minimum of 27 participants in each group were required for the study to ensure adequate statistical power. To account for potential dropouts, 30 individuals were recruited for each group by including an additional 10% of the original sample size. Prior to the start of the research, two individuals from the OA group were excluded because they withdraw to participate due to decline in general condition. As a result, a total of 58 participants were utilized for the purpose of data analysis.

Statistical analysis

Data were analyzed in SPSS software version 18.0 (SPSS Inc., Quarry Bay, Hong Kong). In the analysis, the mean of the two trials conducted by each subject was calculated. The participants’ characteristics, including demographic data and anthropometric measurements, were presented using descriptive statistics such as means and standard deviations (SD). Shapiro-Wilk test was used to verify that the data followed a normal distribution. A paired t-test was utilized to analyze the spatio-temporal parameters of the remaining nine conditions in comparison to the Natural condition separately for each of the two groups: YA and OA. The significance level α was set at p < 0.05.

The general characteristics of the subjects are shown in Table 2.

(Table 2)

Table 3 presents the gait kinematic and spatio-temporal parameters for participants using the assist mode of the Bot Fit. In the YA group, the use of Bot Fit in W A1, W A3, and W A5 modes significantly increased T R compared to W/O A: W A1 (6.25°), W A3 (6.82°), and W A5 (7.23°). O R showed significant increases in W A3 (11.22°) and W A5 (11.36°) compared to W/O A. R R significantly increased in W A1 (20.35°), W A3 (21.92°), and W A5 (22.94°) compared to W/O A. SLD significantly decreased in W A1 (1.62 cm) compared to W/O A. GS significantly increased in W A1 (1.33 m/s), W A3 (1.35 m/s), and W A5 (1.37 m/s) compared to W/O A. SL significantly increased in W A1 (0.69 m), W A3 (0.70 m), and W A5 (0.70 m) compared to W/O A. SPR significantly increased in W A5 (40.53%) compared to W/O A. In the OA group, T R significantly increased in W A1 (6.20°), W A3 (6.80°), and W A5 (6.72°) compared to W/O A. O R significantly increased in W A1 (10.68°), W A3 (11.12°), and W A5 (11.10°) compared to W/O A. R R significantly increased in W A1 (19.83°), W A3 (21.45°), and W A5 (21.65°) compared to W/O A. GS significantly increased in W A1 (1.27 m/s), W A3 (1.24 m/s), and W A5 (1.26 m/s) compared to W/O A. SL significantly increased in W A1 (0.65 m), W A3 (0.64 m), and W A5 (0.65 m) compared to W/O A.

(Table 3)

Table 4 presents the gait kinematic and spatio-temporal parameters for participants in voice coach. In the YD group, W VC2 and W VC3 modes significantly increased T R compared to W/O A: W VC2 (6.61°) and W VC3 (7.00°). O R showed significant increases in W VC1 (11.19°), W VC2 (11.74°), and W VC3 (12.42°) compared to W/O A. R R significantly increased in W VC2 (29.03°) and W VC3 (33.47°) compared to W/O A. SLD significantly decreased in W VC1 (1.73 cm) compared to W/O A. GS significantly increased in W VC1 (1.40 m/s), W VC2 (1.42 m/s), and W VC3 (1.53 m/s) compared to W/O A. SL significantly increased in W VC1 (0.77 m), W VC2 (0.77 m), and W VC3 (0.85 m) compared to W/O A. SPR significantly increased in W VC2 (41.01%) and W VC3 (41.38%) compared to W/O A. In the OD group, T R significantly increased in W VC1 (6.66°), W VC2 (6.57°), and W VC3 (7.24°) compared to W/O A. O R significantly increased in W VC1 (11.49°), W VC2 (11.7°), and W VC3 (12.9°) compared to W/O A. R R significantly increased in W VC1 (24.39°), W VC2 (25.13°), and W VC3 (29.73°) compared to W/O A. GS significantly increased in W VC1 (1.31 m/s), W VC2 (1.35 m/s), and W VC3 (1.41 m/s) compared to W/O A. SL significantly increased in W VC1 (0.69 m), W VC2 (0.69 m), and W VC3 (0.72 m) compared to W/O A.

(Table 4)

(Fig. 3)

(Fig. 4)

The purpose of this study was to analyze the effect of assist mode, one of the functions of the wearable robot Bot Fit, on walking and the additional effect of voice coach. Young people working in modern society pursue efficiency. They also prefer efficient exercise, that is, they prefer to produce a lot of effects in a short exercise time. Walking is an exercise that can be easily accessed by people of all ages, and therefore efficient walking is the optimal method from this perspective. How efficiently you are progressing during walking exercise can be measured through various elements of walking. For example, exercise amount can increase as speed, stride length, and swing phase ratio increase. In addition, pelvic movement in an appropriate range for the speed and symmetry of both lower extremities for the stride length lead to gait with correct posture and increase gait efficiency. Based on the characteristics of these walking elements, Bot fit's assist mode brings exercise benefits to users.

The movement of the pelvis along the three axes during walking significantly increased with assist mode of Bot Fit compared to the without Bot Fit condition. In other studies, the rotational movement of the pelvis is considered important in terms of increasing stride length in the development of humanoid robots¹⁹. Obliquity movements of the pelvis control the COM and are active when the gluteus muscle is normally active²⁰. In a previous study, it was confirmed that the activity of gluteus medius, gluteus maximus, and erector spinae muscle activity decreased along with the increased activity of rectus abdominus during walking when applying GEMS, the original version of Bot Fit⁶. These results show that in this study, Bot Fit regulates com by playing the role of gluteus medius, and as a result, pelvic obliquity increased. In addition, the difference between left and right step length was significantly reduced. Movements of the three axes of the pelvis are all significantly correlated with walking speed²¹.

There is a positive correlation between pelvic posterior tilt and posterior shift of the COM²². And the center of pressure is positively correlated with the movement of the COM²³. Based on this evidence, it can be seen that the Bot Fit has a control pack behind the waist, rectus abdominus is naturally activated to prevent the COM from going backwards, resulting in posterior tilt. Additionally, one study showed that coordination between the pelvis and hip joints plays an important role when walking, and that it stabilizes walking motion and minimizes energy consumption by controlling the activity of the hip muscles²⁴. These results suggest that Bot fit's assist mode can induce efficient walking exercise by correcting the user's posture while walking.

In general, as walking speed increases, pelvic movement in three planes increases and the swing phase ratio also increases²⁵. In this study, the swing phase ratio tended to increase when using assist mode of Bot Fit. Based on previous research, it can be seen that a significant increase in speed led to an increase in swing phase ratio. In particular, an increase in swing phase ratio can be said to lead to walking with lighter steps and more vigorous exercise from an exercise perspective. Therefore, when wearing Bot Fit using assist mode, rectus abdominus activation increases as the COM rearward moves, tilt range increases, and obliquity increases due to gluteus muscle assistance, which reduces COM sway, leading to stable walking, and the assistive torque of the hip joint increases the rotation range increasing the stride length and consequently the speed.

In the case of voice coach, the increase in speed, stride size, pelvic movement, and swing phase ratio was greater than with assist mode application. This can be seen as the intended improvement of pedestrians through direct voice coach instructions. Voice coach based on such direct instructions is meaningful in that it can motivate users while exercising. However, the difference between left and right strides was further reduced in assist mode. This phenomenon is possible because the same torque is given to both actuators of the Bot fit. Therefore, it is believed that if voice coach and assist mode are appropriately combined, the best performance will be achieved in terms of walking posture and energetic exercise. The voice coach comments in this study had different intensities, and if the increased stride length data for each comment is utilized, the real-time step increase rate can be sensed and a customized voice coach comment can be applied accordingly. Therefore, if voice coach and the unique capabilities of assist mode are appropriately combined, human robot interaction, a trend in wearable robots, can be possible. Through this interaction, robot users will be able to exercise effectively and efficiently.

Bot Fit’s assist mode has a positive effect on walking efficiency and posture, and voice coach is a suitable method for increasing speed and stride length. Therefore, proper use of assist mode and voice coach can increase the effectiveness of walking exercise using Bot Fit. For further study, it is necessary to verify the effect during long-term exercise and in patients with physical disabilities.

COM: Center of mass

BMI: Body mass index

IMU: Inertial measurement unit

YA: Young adults

OA: Older adults

W A1: With Bot Fit assist mode 1

W A3: With Bot Fit assist mode 3

W A5: With Bot Fit assist mode 5

W VC1: With Voice Coach 1

W VC2: With Voice Coach 2

W VC3: With Voice Coach 3

T R: Tilt Range

O R: Obliquity Range

R R: Rotation range

SLD: Step Length Differential

GS: Gait Speed

SL: Step Length

SPR: Swing Phase Ratio

SD: Standard deviation

Ethics approval and consent to participate

The study was performed in accordance with the Declaration of Helsinki. The study was approved by the Institutional Review Board of Sahmyook University (IRB No. SYU 2022-10-009). Informed consent was obtained from all participants prior to the study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by the Samsung Electronics (202200910001).

Authors’ contributions

J.H.S conceived the experiments.

J.H.S., N.E.B. performed the experiments. H.J.L., D.K.L. and D. W. K. provided the materials. J. H. S analyzed and interpreted the data. W.H.L supervised the experiments. J.H.S., N.E.B., and H. J. Y. wrote the manuscript. All the authors have read and approved the final version of the manuscript.

Acknowledgements

We express our gratitude to Samsung Electronics for their assistance in utilizing the Bot Fit. We would also like to thank all the subjects who participated in this study.

Shin J-h, Byeon N, Yu H, et al. Effect of 4-weeks exercise program using wearable hip-assist robot (EX1) in older adults: one group pre-and post-test. BMC Geriatr. 2023;23(1):724.
Shin J-h, Byeon N, Yu H, et al. Morphological Changes in Quadriceps Muscles through 3-Week Combined Exercise using a Wearable Robot (EX1) in Young Adult. Physical Therapy Rehabilitation Science. 2023;12(1):33-42.
Shin J-h, Park H-K, Jung J, et al. A Study on Energy Efficiency in Walking and Stair Climbing for Elderly Wearing Complex Muscle Support System. Physical Therapy Rehabilitation Science. 2022;11(4):478-487.
Lenzi T, Vitiello N, De Rossi SMM, et al. Measuring human–robot interaction on wearable robots: A distributed approach. Mechatronics. 2011;21(6):1123-1131.
Diaz MA, Voss M, Dillen A, et al. Human-in-the-loop optimization of wearable robotic devices to improve human–robot interaction: A systematic review. IEEE Transactions on Cybernetics. 2022;53(12):7483-7496.
Lee H-J, Lee S, Chang WH, et al. A wearable hip assist robot can improve gait function and cardiopulmonary metabolic efficiency in elderly adults. IEEE Trans Neural Syst Rehabil Eng. 2017;25(9):1549-1557.
Huo W, Mohammed S, Moreno JC, Amirat Y. Lower limb wearable robots for assistance and rehabilitation: A state of the art. IEEE systems Journal. 2014;10(3):1068-1081.
Lee S-H, Kim J, Lim B, Lee H-J, Kim Y-H. Exercise with a wearable hip-assist robot improved physical function and walking efficiency in older adults. Sci Rep. 2023;13(1):7269.
Whittle MW, Levine D. Three-dimensional relationships between the movements of the pelvis and lumbar spine during normal gait. Human movement science. 1999;18(5):681-692.
Lewis CL, Laudicina NM, Khuu A, Loverro KL. The human pelvis: variation in structure and function during gait. The Anatomical Record. 2017;300(4):633-642.
Yoder AJ, Petrella AJ, Silverman AK. Trunk–pelvis motion, joint loads, and muscle forces during walking with a transtibial amputation. Gait Posture. 2015;41(3):757-762.
Canton S, MacLellan MJ. Active and passive contributions to arm swing: Implications of the restriction of pelvis motion during human locomotion. Human movement science. 2018;57:314-323.
Balasubramanian CK, Bowden MG, Neptune RR, Kautz SA. Relationship between step length asymmetry and walking performance in subjects with chronic hemiparesis. Arch Phys Med Rehabil. 2007;88(1):43-49.
Mills K, Hettinga BA, Pohl MB, Ferber R. Between-limb kinematic asymmetry during gait in unilateral and bilateral mild to moderate knee osteoarthritis. Arch Phys Med Rehabil. 2013;94(11):2241-2247.
Stern KR. An evaluation of written, graphics, and voice messages in proceduralized instructions. SAGE Publications Sage CA: Los Angeles, CA; 1984:314-318.
Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. The lancet. 2007;370(9596):1453-1457.
Bugané F, Benedetti MG, Casadio G, et al. Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: Validation on normal subjects by standard gait analysis. Comput Methods Programs Biomed. 2012;108(1):129-137.
Casabona A, Valle MS, Mangano GRA, Cioni M. Identifying the Effects of Age and Speed on Whole-Body Gait Symmetry by Using a Single Wearable Sensor. Sensors. 2022;22(13):5001.
Park B, Park J. Optimization of Pelvic Rotation Walking Pattern Considering Future States Using Model Predictive Control to Increase the Step Length. IEEE Access. 2022;10:44471-44482.
Kim D, Unger J, Lanovaz JL, Oates AR. The relationship of anticipatory gluteus medius activity to pelvic and knee stability in the transition to single-leg stance. Pm&r. 2016;8(2):138-144.
Lee KS. Study of Corrdlation between gait velocity and angle of pelvic of elderly women. J Korean Soc Neur Ther. 2013;17:15-23.
Wada O, Tateuchi H, Ichihashi N. The correlation between movement of the center of mass and the kinematics of the spine, pelvis, and hip joints during body rotation. Gait Posture. 2014;39(1):60-64.
Choi H, Kim Y. The relationship between the COP–COM variable and the horizontal acceleration of the body in postural sway, falling and walking. Springer; 2009:281-283.
Crosbie J, Vachalathiti R. Synchrony of pelvic and hip joint motion during walking. Gait Posture. 1997;6(3):237-248.
Stansfield B, Hawkins K, Adams S, Bhatt H. A mixed linear modelling characterisation of gender and speed related changes in spatiotemporal and kinematic characteristics of gait across a wide speed range in healthy adults. Med Eng Phys. 2018;60:94-102.

No competing interests reported.

Download PDF

Reviews received at journal
15 Sep, 2024
Reviewers agreed at journal
10 Sep, 2024
Reviewers agreed at journal
08 Sep, 2024
Reviewers invited by journal
10 Aug, 2024
Editor invited by journal
09 Aug, 2024
Editor assigned by journal
10 Jul, 2024
Submission checks completed at journal
10 Jul, 2024
First submitted to journal
09 Jul, 2024

You are reading this latest preprint version

Effect of wearable robot Bot Fit’s hip joint-centered assist torque and voice coach on walking

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Trial registration

Figures

Background

Methods

Participants

Wearable robot

(Fig. 1. Bot Fit)

Experimental Procedures

(Table 1)

(Fig. 2. Outline of the 20-meter distance)

Data analysis

Sample size

Statistical analysis

Results

(Table 2)

(Table 3)

(Table 4)

(Fig. 3)

(Fig. 4)

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1