Estimating energy expenditure is essential in monitoring the intensity of physical activity and health status. Energy expenditure can be estimated based on wearable sensors such as inertial measurement unit (IMU). While a variety of methods have been developed to estimate energy expenditure during day-to-day activities, their performances have not been thoroughly evaluated under walking conditions according to various speeds and inclines. This study investigated IMU-based neural network models for energy expenditure estimation under various walking conditions and comparatively analyzed their performances in terms of sensor attachment locations and training/testing datasets. In this study, two neural network models were selected based on a previous study (Slade et al., 2019): (M1) a multilayer perceptron using sensor signals during each gait cycle, and (M2) a recurrent neural network using sensor signal sequences of a fixed window size. The results revealed the following: (i) the performance of the foot attachment model was the best among the five sensor attachment locations (0.89 W/kg for M1 and 1.14 W/kg for M2); and (ii) although the performance of M1 was superior to that of M2, M1 requires accurate gait detection for data segmentation by each stride, which hinders the usefulness of M2.
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Development of a Novel Ventilation Estimation Model Based on Convolutional Neural Network (CNN) Jeongyeon Chu, Jaehyon Baik, Kangsu Jeong, Seungwon Jung, Youngjin Park, Hosu Lee Journal of Korea Robotics Society.2025; 20(1): 138. CrossRef
In highly mobile workplaces, wearable walking assistant robots can reduce muscle fatigue in the lower extremities of workers and increase energy efficiency. In this study, walking efficiency according to the development of an ultralight wearable hip-assist robot for industrial workers was verified. Five healthy adult males participated in this study. Their muscle fatigue and energy consumption were compared with and without the robot while walking on a flat treadmill and stairs. When walking on the treadmill while wearing the robot, muscle fatigue in the rectus femoris and gastrocnemius decreased by 90.2% and 37.7%, respectively. Oxygen uptake and energy expenditure per minute also decreased by 8.9% and 13.1%, respectively. When climbing stairs while wearing the robot, fatigue of the tibialis anterior, semitendinosus, and gastrocnemius muscles decreased by 18.2%, 33.3%, and 63.6%, respectively. Oxygen uptake and energy expenditure per minute also decreased by 3.6% and 3.7%, respectively. Although wearing a hip-assist robot could reduce muscle fatigue and use metabolic energy more efficiently, it is necessary to further increase the energy efficiency while climbing stairs. This study is intended to provide basic data to improve the performance of robots.
Hybrid mobile robot is the system that will practically combine legged walking and skated driving in the same system. Therefore, this robot has own problems of inverse kinematics that are not considered in typical walking robots. In this paper, I fully categorized the inverse kinematics problems for hybrid mobile robot with general motion by walking and driving on an inclined plane, including switching end-effectors between foots and blades. I also solved the inverse kinematics for each case of problems. I here actively adopted the coordinate transformation derived from the inclined plane to cope with the random motion of foots and blades on the plane. I then presented several examples of the inverse kinematics problems with specific situations, and verified the validity of the analysis method from the results.
Herein, we describe the development of a wearable lower limb rehabilitation robot that can perform walking movement according to the walking pattern trajectory. The robot can adjust the left and right widths of the waist and the front and rear widths of 100 and 20 mm, and the length of the thigh link and calf link by 100 and 80 mm, respectively, so that stroke patients of different heights and weights can use it in hospitals. For manufacturing the lower limb rehabilitation robot, the right exoskeleton was safely designed through structural analysis, and the motor and reducer constituting the hip joint actuator were calculated. The fabricated lower limb rehabilitation robot was divided into its own characteristic experiment and wearing characteristic experiment. Its own characteristic experiment was an experiment by the robot itself, and the wearing characteristic experiment was an experiment conducted after a person wears the robot. Through these two experiments, angular deviation of the walking pattern was analyzed. Results of the analysis confirmed that the wearable walking characteristic test was performed within 3.1° based on the self walking characteristic test result. Therefore, the fabricated lower limb rehabilitation robot can be used for gait training in stroke patients.
In this paper, an integrated ankle torque sensor and mechanism (Foot Link) of a Tendon driven-type wearing walking aid robot were designed. The foot link consists of an ankle torque sensor and a mechanism connected to the footrest. The size of the sensing part of the ankle torque sensor was designed through structural analysis and assembled by attaching a strain gauge. As a result, the reproducibility error and the nonlinearity error were within 0.04%, respectively. And the calibration result of the ankle torque sensor, reproducibility error, and non-linearity error were identified to be within 1%, respectively. Therefore, it is proposed that the ankle torque sensor presented in this paper can be used to measure the torque acting on the tendon-driven walking aid robot.
In this paper, the design and fabrication of the calf-link with knee joint torque sensor of a tandem-driven walking-assist robot is described. Tendon-driven walking-assist robots should be designed and constructed with a wire wheel and a torque sensor, as one body to reduce the weight of the calf link. The torque sensor consists of four plate sensing parts crossed 90° around the wire wheel. Structural analysis was performed to determine the size of the torque sensor sensing part, and a torque sensor was built by attaching a strain gauge to the sensing part. As a result of the characteristics test, the reproducibility error and the nonlinearity error of the manufactured torque sensor were less than 0.03% and 0.04%, respectively. As a result of the calibration, the reproducibility error and the nonlinearity error were less than 0.08%, respectively. Thus, it is considered that the knee joint torque sensor of the calf link can be attached to the tandem-driven walking-assist robot.
[Objective] The objective of this study was to investigate the reliability of smartphone-based measurements of the upper body, thigh, and shin segmental angles, and the hip and knee joint angles when walking. [Method] The sample size of this study included eight young and healthy college students. In this study, smartphones were used to determine the changes in angles when the subjects walked with smartphones attached to their torso (upper body), thigh, and shin. The obtained angles represented segmental angles for the torso, thigh, and shin, and were later used to calculate hip and knee joint angles. Measurements were taken and then the test-retest method was used to evaluate the agreement between the test and retest results. [Results] According to the results, a very high reliability for the torso and shin segmental angles (ICC>0.75) and a high reliability for the thigh segmental angle and hip and knee joint angles (ICC>0.60) were displayed. [Conclusion] According to the results of this study, it was established that smartphones can be sufficiently used as devices for gait analysis.
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Previous studies on joint angle estimation have been restricted to slow-speed level walking conditions, even though slope walking and running elicit unique biomechanical characteristics. Measurements were mostly based on an optical motion capture system despite in-the-lab limitation of measurement technique. The contribution of this study is twofold: (i) to propose a joint angle estimation method by applying a state-of-the-art parallel Kalman filter based on an inertial measurement unit (IMU) that can overcome in-the-lab limitation, and (ii) to demonstrate its application to level walking condition as well as slope walking and running conditions to fill a gap in joint kinematics literature. In particular, this study focuses on knee flexion/extension and ankle dorsiflexion/plantarflexion angles at various speed variations. The parallel Kalman filter applied in the proposed method can compensate external acceleration through Markov-chain-based acceleration modeling, that may enhance joint estimation performance in high speed walking conditions. To validate the proposed estimation method, an optical motion capture system was used as reference. In addition, patterns for each condition were investigated to identify and evaluate presence of classifying features.
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This paper proposes a walking position tracking method using inertial measurement unit (IMU) based on kinematic model of human body and walking cycle analysis. A kinematic model of lower body consisting of 9 coordinate frames and 7 links is used to estimate walking trajectory of the body based on rotation angles of the lower body measured by IMU. In this method, the position of left or right end frame of the lower body which is in contact with the ground is first identified and set as the reference position. The position of the base frame attached on the center of pelvis is then computed using the kinematic model and the reference position. One can switch the reference position with the position of the other end frame at the moment of heel strike. The proposed position tracking method was experimentally validated. Experimental result showed that position tracking errors were within 1.4% of walking distance for straight walking and 2.2% for circular walking.
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In this paper, we design and fabricate a wearable walking-assist robot using a tendon-driven method. Most wearable walking-assist robots are designed using the method of the attaching of the motors to the hip, knee, and ankle joints. The robot needs the capacities of the motors attached to the hip and knee joints to equal the weights of the motors attached to the knee and ankle joints and the motor attached to the ankle, respectively. To solve these problems, we design and fabricate the wearable walking-assist robot using a tendon-driven method that rotates the joints by attaching the motors of the hip, knee, and ankle joints to the waist joint, and pulling it with a line. The gait patterns of a normal person are photographed and analyzed, thereby providing the ankle position (x, y) during the walking that is then calculated using the forward kinematic equation, while each joint angle is calculated using the inverse kinematic equation. As a result of the characteristic experiment of the wearable walking-assist robot, the resultant walking aspect is similar to that of the normal person.
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In this paper, we designed and fabricated a hip joint torque sensor that can measure the torque applied to the hip joint of a walking assistant robot that can be used by a leg patient. To do this, we modeled the structure of the hip joint torque sensor so that it can be connected to the thigh link and the body of the walking assist robot. We calculated the rated torque of the hip joint torque sensor using computer simulation and determined the size of the torque sensor using a finite element program. The hip joint torque sensor was made by constructing a Wheatstone bridge and attaching a strain gauge. The characteristic test of the fabricated torque sensor was performed using a calibration device, and the reproducibility error and the nonlinearity error of the torque sensor were both less than 0.04%. Therefore, it is proposed that the developed hip joint torque sensor can be attached to the thigh link of the wearable walking assist robot, and the torque sensor can accurately measure the torque applied to the hip joint.
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A depth image camera is used for efficient estimation of walking intention of a pedestrian. Three-Dimensional image coordinates of the pedestrian’s joints are obtained from the image data that includes depth information and are converted into the absolute coordinate values. The absolute coordinate data are classified and matched with all 20 joints of a pedestrian and the 9 joints that are corresponding the lower limbs are finally selected. After calculating each three-dimensional area of a triangle that was formed with the adjacent 3 joints of the 9 lower limb joints, the centroid of all triangles along time is obtained. The walking intention, that includes the direction and the speed of walking, can be estimated with the change rate of this centroid. It is experimentally verified by comparing the distance that is measured with inertia moment unit and the distance that the calculated centroid is moving.
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