Optical Motion Capture System Research Articles

Influence of cognitive and motor tasks using smartphone during gait: EMG and gait performance analysis – Dual-task study

Previous studies reported changes in spatiotemporal gait parameters during dual-task performance while walking using a smartphone compared to walking without a smartphone. However, studies that assess muscle activity while walking and simultaneously performing smartphone tasks are scarce. So, this study aimed to assess the effects of motor and cognitive tasks using a smartphone while simultaneously performing gait on muscle activity and gait spatiotemporal parameters in healthy young adults. Thirty young adults (22.83 ± 3.92 years) performed five tasks: walking without a smartphone (single-task, ST); typing on a smartphone keyboard in a sitting position (secondary motor single-task); performing a cognitive task on a smartphone in a sitting position (cognitive single-task); walking while typing on a smartphone keyboard (motor dual-task, mot-DT) and walking while performing a cognitive task on a smartphone (cognitive dual-task, cog-DT). Gait speed, stride length, stride width and cycle time were collected using an optical motion capture system coupled with two force plates. Muscle activity was recorded using surface electromyographic signals from bilateral biceps femoris, rectus femoris, tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis, gluteus maximus and lumbar erector spinae. Results showed a decrease in stride length and gait speed from the single-task to cog-DT and mot-DT (p < 0.05). On the other hand, muscle activity increased in most muscles analyzed from single- to dual-task conditions (p < 0.05). In conclusion, performing a cognitive or motor task using a smartphone while walking promote a decline in spatiotemporal gait parameters performance and change muscle activity pattern compared to normal walking.

Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data.

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) techniques are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one typically uses an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, an ML approach is presented that maps experimentally recorded IMC input data to the human upper-extremity MSK model outputs computed from ('gold standard') OMC input data. Essentially, this proof-of-concept study aims to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and a comprehensive search for the best-fit model in the hyperparameters space in both subject-exposed (SE) as well as subject-naive (SN) settings. We observed a comparable performance for both FFNN and RNN models, which have a high degree of agreement (ravg,SE,FFNN=0.90±0.19, ravg,SE,RNN=0.89±0.17, ravg,SN,FFNN=0.84±0.23, and ravg,SN,RNN=0.78±0.23) with the desired OMC-driven MSK estimates for held-out test data. The findings demonstrate that mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from 'lab to field'.

Validation of Alogo Move Pro: A GPS-Based Inertial Measurement Unit for the Objective Examination of Gait and Jumping in Horses.

Quantitative information on how well a horse clears a jump has great potential to support the rider in improving the horse's jumping performance. This study investigated the validation of a GPS-based inertial measurement unit, namely Alogo Move Pro, compared with a traditional optical motion capture system. Accuracy and precision of the three jumping characteristics of maximum height (Zmax), stride/jump length (lhorz), and mean horizontal speed (vhorz) were compared. Eleven horse-rider pairs repeated two identical jumps (an upright and an oxer fence) several times (n = 6 to 10) at different heights in a 20 × 60 m tent arena. The ground was a fiber sand surface. The 24 OMC (Oqus 7+, Qualisys) cameras were rigged on aluminum rails suspended 3 m above the ground. The Alogo sensor was placed in a pocket on the protective plate of the saddle girth. Reflective markers placed on and around the Alogo sensor were used to define a rigid body for kinematic analysis. The Alogo sensor data were collected and processed using the Alogo proprietary software; stride-matched OMC data were collected using Qualisys Track Manager and post-processed in Python. Residual analysis and Bland-Altman plots were performed in Python. The Alogo sensor provided measures with relative accuracy in the range of 10.5-20.7% for stride segments and 5.5-29.2% for jump segments. Regarding relative precision, we obtained values in the range of 6.3-14.5% for stride segments and 2.8-18.2% for jump segments. These accuracy differences were deemed good under field study conditions where GPS signal strength might have been suboptimal.

DOES KINEMATICALLY ALIGNED TOTAL KNEE ARTHROPLASTY RESTORE NATIVE JOINT LAXITY BETTER THAN MECHANICAL ALIGNMENT? A CADAVERIC STUDY

Mechanical alignment (MA) in total knee arthroplasty (TKA), although considered the gold standard, reportedly has up to 25% of patients expressing post-operative dissatisfaction. Biomechanical outcomes following kinematic alignment (KA) in TKA, developed to restore native joint alignment, remain unclear. Without a clear consensus for the optimal alignment strategy during TKA, the purpose of this study was to conduct a paired biomechanical comparison of MA and KA in TKA by experimentally quantifying joint laxity and medial collateral ligament (MCL) strain.14 bilateral native fresh-frozen cadaveric lower limbs underwent medially-stabilised TKA (GMK Sphere, Medacta, Switzerland) using computed CT-based subject-specific guides, with KA and MA performed on left and right legs, respectively. Each specimen was subjected to sensor-controlled mediolateral laxity tests. A handheld force sensor (Mark-10, USA) was used to generate an abduction-adduction moment of 10Nm at the knee at fixed flexion angles (0°, 30°, 60°, 90°). A digital image correlation system was used to compute the strain on the superficial medial collateral ligament. A six-camera optical motion capture system (Vicon MX+, UK) was used to acquire kinematics using a pre-defined CT-based anatomical coordinate system. A linear mixed model and Tukey's posthoc test were performed to compare native, KA and MA conditions (p&lt;0.05).Unlike MA, medial joint laxity in KA was similar to the native condition; however, no significant difference was found at any flexion angle (p&gt;0.08). Likewise, KA was comparable with the native condition for lateral joint laxity, except at 30°, and no statistical difference was observed. Although joint laxity in MA seemed lower than the native condition, this difference was significant only for 30° flexion (p=0.01). Both KA and MA exhibited smaller MCL strain at 0° and 30°; however, all conditions were similar at 60° and 90°.Medial and lateral joint laxity seemed to have been restored better following KA than MA; however, KA did not outperform MA in MCL strain, especially after mid-flexion. Although this study provides only preliminary indications regarding the optimal alignment strategy to restore native kinematics following TKA, further research in postoperative joint biomechanics for load bearing conditions is warranted.

Design and Optimization of Lower Limb Rehabilitation Exoskeleton with a Multiaxial Knee Joint.

To facilitate rehabilitation training for patients, we proposed the implementation of an anthropomorphic exoskeleton structure that incorporates a variable instantaneous center of rotation (ICR). This design considers the variability in knee ICR among individuals, resulting from the irregular form of the human knee joint, and leverages a double-degrees-of-freedom (2DOF) five-bar mechanism to adapt to these differences. The walking gait of the human lower limb and the corresponding knee ICR were measured and calculated using an optical 3D motion capture system. The optimal dimension parameters of the five-bar mechanism were then obtained through the optimization of human movement position inputs and rod length constraints to minimize the error in knee ICR, gait angle, and ankle trajectory between the human and the exoskeleton. Finally, we established an exoskeleton prototype to conduct relevant experimental tests. The experiment results showed that the average errors of knee ICR trajectory, hip angle, knee angle, and ankle trajectory were 5.52 × 10-4 m, 0.010 rad, 0.014 rad, and 1.57 × 10-3 m, respectively. The experimental results demonstrated that the exoskeleton's movement trajectory was close to the human's, reducing the human-mechanism interaction force and improving patient comfort during rehabilitation training.

Benchmarking Dataset of Signals from a Commercial MEMS Magnetic-Angular Rate-Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion.

In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic-angular rate-gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset contains 30 files resulting from different volunteer subjects executing manipulations of the MARG in areas with and without magnetic distortion. Each file also contains reference ("ground truth") MARG orientations (as quaternions) determined by an optical motion capture system during the recording of the MARG signals. The creation of FIUMARGDB responds to the increasing need for the objective comparison of the performance of MARG orientation estimation algorithms, using the same inputs (accelerometer, gyroscope, and magnetometer signals) recorded under varied circumstances, as MARG modules hold great promise for human motion tracking applications. This dataset specifically addresses the need to study and manage the degradation of orientation estimates that occur when MARGs operate in regions with known magnetic field distortions. To our knowledge, no other dataset with these characteristics is currently available. FIUMARGDB can be accessed through the URL indicated in the conclusions section. It is our hope that the availability of this dataset will lead to the development of orientation estimation algorithms that are more resilient to magnetic distortions, for the benefit of fields as diverse as human-computer interaction, kinesiology, motor rehabilitation, etc.

Haptify: A Measurement-Based Benchmarking System for Grounded Force-Feedback Devices

Grounded force-feedback (GFF) devices are an established and diverse class of haptic technology based on robotic arms. However, the number of designs and how they are specified make comparing devices difficult. We thus present Haptify, a benchmarking system that can thoroughly, fairly, and noninvasively evaluate GFF haptic devices. The user holds the instrumented device end-effector and moves it through a series of passive and active experiments. Haptify records the interaction between the hand, device, and ground with a seven-camera optical motion-capture system, a 60-cm-square custom force plate, and a customized sensing end-effector. We demonstrate six key ways to assess GFF device performance: workspace shape, global free-space forces, global free-space vibrations, local dynamic forces and torques, frictionless surface rendering, and stiffness rendering. We then use Haptify to benchmark two commercial haptic devices. With a smaller workspace than the 3D Systems Touch, the more expensive Touch X outputs smaller free-space forces and vibrations, smaller and more predictable dynamic forces and torques, and higher-quality renderings of a frictionless surface and high stiffness.

Estimation of hike events and temporal parameters with body-attached sensors

The analysis of human gait is of fundamental importance for the monitoring and enhancement of athletes’ performances. The kinematics and kinetics of human gait are mostly investigated with optical motion capture systems and force plates that require specialised laboratories and limit the possible test conditions. On the contrary, body-attached sensor networks provide an opportunity for long-term acquisitions in unsupervised, naturalistic scenarios. In this study, a wearable sensor network consisting of two wireless dataloggers and two instrumented insoles with eight pressure sensors each is used. Custom algorithms for the automatic detection of hike events and the estimation of the related temporal parameters based on sensors data are presented. The proposed algorithms were tested against laboratory measurements performed on an instrumented treadmill and showed relative errors of less than 2.5% in the estimation of stride time, step time and cadence. Higher relative errors were found in the estimation of stance and swing phases. The developed algorithms were also applied in a field study. In this paper data from one subject are considered. The aim of this research work is to provide an effective sensor-based methodology for the evaluation of gait parameters in naturalistic settings.

Upper Body Pose Estimation Using a Visual–Inertial Sensor System With Automatic Sensor-to-Segment Calibration

Upper body kinematics is essential for motor function assessment and robot-assisted rehabilitation training. Wearable sensor systems, such as inertial measurement units (IMUs), provide affordable solutions to replace laboratory-based motion capture systems for use in daily life. However, the sensor-to-segment calibration often relies on predefined posture or movements, which is hard to perform accurately, particularly for patients with a limited range of motion. A visual–inertial sensor system is presented, which includes three sensor modules attached to the trunk, upper arm, and forearm. Each module has an IMU and an ArUco marker, which can be captured by a camera and the driftless orientation of the modules is computed from visual–inertial fusion. The sensor-to-segment transformations are calibrated from a period of arbitrary arm movements in either a 2-D plane or 3-D space, simulating the training process assisted by end-effector robots. Experiments were conducted to validate the feasibility and evaluate the accuracy of the proposed method. The estimated shoulder and elbow joint angles correlated well ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$ </tex-math></inline-formula> 0.986) with the ground truth from the optical motion capture (OMC) system. The joint angles presented low root-mean-square errors (RMSEs) ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt; 4^{\circ }$ </tex-math></inline-formula> ) except for the forearm pronation–supination angle (9.34°), which relied on manual alignment. The sensor system provides a simple and easy-to-use solution for movement assessment during robot-assisted training.

Accuracy Verification of Four-Dimensional CT Analysis of Knee Joint Movements: A Pilot Study Using a Knee Joint Model and Motion-Capture System.

Objective This study aimed to use the optical motion-capture method to verify the accuracy of four-dimensional computed tomography (4D-CT) analysis of knee joint movement. Methods One static CT and three 4D-CT examinations of the knee joint model were obtained. The knee joint model was passively moved in the CT gantry during 4D-CT acquisitions. 4D-CT and static CT examinations were matched to perform 3D-3D registration. An optical-motion capture system recorded the position-posture of the knee joint model simultaneously with the 4D-CT acquisitions. Reference axes (X, Y, and Z directions) were defined based on static CT and applied to 4D-CT and the optical-motion capture system. Using the position-posture of the motion capture system as a reference standard, the position-posture measurements using 4D-CT were compared to these values, and the accuracy of the 4D-CT analysis of knee joint movements was quantitatively assessed. Results The position-posture measurements obtained from 4D-CT showed a similar tendency to those obtained from the motion-capture system. In the femorotibial joint, the difference in the spatial orientation between the two measurements was 0.7 mm in the X direction, 0.9 mm in the Y direction, and 2.8 mm in the Z direction. The difference in angle was 1.9° in the varus/valgus direction, 1.1° in the internal/external rotation, and 1.8° in extension/flexion. In the patellofemoral joint, the difference between the two measurements was 0.9 mm in the X direction, 1.3 mm in the Y direction, and 1.2 mm in the Z direction. The difference in angle was 0.9° for varus/valgus, 1.1° for internal/external rotation, and 1.3° for extension/flexion. Conclusions 4D-CT with 3D-3D registration could record the position-posture of knee joint movements with an error of less than 3 mm and less than 2° when compared with the highly accurate optical-motion capture system. Knee joint movement analysis using 4D-CT with 3D-3D registration showed excellent accuracy for in vivo applications.

Measurement of Trunk Movement during Sit-to-Stand Motion Using Laser Range Finders: A Preliminary Study.

The sit-to-stand (STS) motion evaluates physical functions in frail older adults. Mounting sensors or using a camera is necessary to measure trunk movement during STS motion. Therefore, we developed a simple measurement method by embedding laser range finders in the backrests and seats of chairs that can be used in daily life situations. The objective of this study was to validate the performance of the proposed measurement method in comparison with that of the optical motion capture (MoCap) system during STS motion. The STS motions of three healthy young adults were simultaneously measured under seven conditions using a chair with embedded sensors and the optical MoCap system. We evaluated the waveform similarity, absolute error, and relationship of the trunk joint angular excursions between these measurement methods. The experimental results indicated high waveform similarity in the trunk flexion phase regardless of STS conditions. Furthermore, a strong relationship was observed between the two measurement methods with respect to the angular excursion of the trunk flexion. Although the angular excursion of the trunk extension exhibited a large error, the developed chair with embedded sensors evaluated trunk flexion during the STS motion, which is a characteristic of frail older adults.

There is no rush to upgrade the tennis racket in young intermediate competitive players: The effects of scaling racket on serve biomechanics and performance.

Scaling the equipment of young athletes is justified by the constraints-led approach introduced in motor learning. The aim of the present study is to analyze the effect of racket scaling on the serve biomechanics and performance parameters for young tennis players (between 8 and 11 years-old). Nine young intermediate competitive tennis players (age: 9.9 ± 1.0 years) performed maximal effort flat serves with three different rackets (scaled 23 inches, scaled 25 inches and full-size 27 inches) in a randomized order. A radar measured ball speed while shoulder and elbow kinetics and upper and lower limb kinematics were calculated with a 20-camera optical motion capture system. Repeated measures ANOVAs were used to analyze the effect of the three rackets on ball speed, percentage of serve in, serve kinematics and kinetics. No significant differences in ball speed, maximal racket head velocity and percentage of serve in were observed between the three rackets. The lowest maximal upper limb kinetics and the highest upper limb maximal angular velocities were obtained with the scaled 23 inches racket. Using scaled rackets has the advantage to decrease shoulder and elbow loadings without reducing serve performance. Consequently, the present results incite tennis coaches and parents to not upgrade too soon the size of the racket in young intermediate tennis players to avoid overuse injury risks in the long term. Our results showed that the full-size 27 inches racket induced higher lower limb kinematics. As a consequence, occasionally serving with a fullsize racket can be a sparingly interesting intervention to help young tennis players to intuitively and immediately increase their leg drive action, allowing a more functional representation of the elite junior serve.

Motion Capture Systems and Their Use in Educational Research: Insights from a Systematic Literature Review

Motion capture is gaining significance in educational research. Different motion capture systems (MCSs) are used in educational research for different purposes and in different ways, which creates a diversity that is difficult to monitor. The aim of this article is to provide an overview of MCSs and their uses in educational research addressing primary and secondary school education. We conducted a systematic review focusing on the types of MCSs and the concrete systems being used, looking at how they are used, the purposes of their use, the aims and the research questions under investigation, the subjects/topics addressed in the studies, the locations/places of use, the potential benefits of using MCSs perceived by the researchers, and the underlying theories of movement. Based on n = 20 studies focusing on primary and secondary school education identified in our database search, we found that these studies mainly used optical MCSs. Furthermore, we identified three main purposes of their use: to directly support the learning process of students, as analysis tools, or for developing tutoring systems that support students in their learning. This paper gives insights into the use of MCSs in educational research and provides a springboard for further research using MCSs in educational research.

Validity and Reliability of Inertial Measurement Unit (IMU)-Derived 3D Joint Kinematics in Persons Wearing Transtibial Prosthesis.

A validity and reliability assessment of inertial measurement unit (IMU)-derived joint angular kinematics during walking is a necessary step for motion analysis in the lower extremity prosthesis user population. This study aimed to assess the accuracy and reliability of an inertial measurement unit (IMU) system compared to an optical motion capture (OMC) system in transtibial prosthesis (TTP) users. Thirty TTP users were recruited and underwent simultaneous motion capture from IMU and OMC systems during walking. Reliability and validity were assessed using intra- and inter-subject variability with standard deviation (S.D.), average S.D., and intraclass correlation coefficient (ICC). The intra-subject S.D. for all rotations of the lower limb joints were less than 1° for both systems. The IMU system had a lower mean S.D. (o), as seen in inter-subject variability. The ICC revealed good to excellent agreement between the two systems for all sagittal kinematic parameters. All joint angular kinematic comparisons supported the IMU system's results as comparable to OMC. The IMU was capable of precise sagittal plane motion data and demonstrated validity and reliability to OMC. These findings evidence that when compared to OMC, an IMU system may serve well in evaluating the gait of lower limb prosthesis users.

Effects of soft tissue artifacts on the calculated kinematics of the knee during walking and running

Kinematics of the knee during gait has mostly been studied using optical motion capture systems (MCS). The presence of soft tissue artifacts (STA) between the skin markers and the underlying bone presents a major impediment to obtaining a reliable joint kinematics assessment. In this study, we determined the effects of STA on the calculation of knee joint kinematics during walking and running, through the combination of high-speed dual fluoroscopic imaging system (DFIS) and magnetic resonance imaging technique. Ten adults walked and ran while data was collected simultaneously from MCS and high-speed DFIS. The study showed that measured STA underestimated knee flexion angle, but overestimated knee external and varus rotation. The absolute error values of the skin markers derived from knee flexion–extension angle, internal-external rotation, and varus-valgus rotation during walking were −3.2 ± 4.3 deg, 4.6 ± 3.1 deg, and 4.5 ± 3.2 deg respectively, and during running were −5.8 ± 5.4 deg, 6.6 ± 3.7 deg, and 4.8 ± 2.5 deg respectively. Average errors relative to the DFIS for flexion–extension angle, internal-external rotation, and varus-valgus rotation were 78 %, 271 %, 265 % during walking respectively, and were 43 %, 106 %, 200 % during running respectively. This study offers reference for the kinematic differences between MCS and high-speed DFIS, and will contribute to optimizing methods for analyzing knee kinematics during walking and running.

A Low-Cost Marker-Based Optical Motion Capture System to Validate Inertial Measurement Units

Commercialized marker-based optical motion capture system like Vicon is very expensive and not widely available. In the development of inertial measurement units, Vicon is required only for validation so sourcing for expensive system is undesirable. In this letter, a low cost alternative, which is also a marker-based optical motion capture system, is proposed to serve the purpose of validating inertial measurement units. Basically, the proposed system uses a smartphone camera to capture the motions of red markers and then track the coordinates of the markers followed by calculating the angle. The proposed system has a low average root mean square error of 0.83° when the distance between camera and markers is 120 cm. The method to synchronize inertial measurement units and camera data is also described in this letter as the starting time and sampling rate of the two systems are most likely different.

Kinematic study of the overall unloading brace for the knee

ObjectiveTraditional knee braces unloading forces primarily from a single compartment are insufficient for patients with knee injuries or knee osteoarthritis (KOA) involving multiple compartments. We investigated how knee kinematics were altered by an overall unloading brace (OUB) designed to unload both the medial and lateral tibiofemoral (TF) compartments simultaneously during dynamic movement. MethodsGait analysis was performed on 32 adults with normal knee alignment and no history of knee disease. Three-dimensional (3D) knee kinematic data collected during treadmill walking (3 km/h) and jogging (5 km/h) with an optical motion capture system were compared with versus without the OUB. ResultsIn the stance phase, wearing the OUB, versus not wearing it, increased the proximal-distal translational range of motion (ROM) of the knee by 4.04 mm (Effect size, ES = 0.97) during walking and by 3.43 mm (ES = 0.97) during jogging, decreased abduction-adduction rotational ROM by 3.09°(ES = 1.05) during walking and by 2.88°(ES = 1.50) during jogging, and decreased internal-external rotation by 2.14°(ES = 0.81) during walking and by 4.66°(ES = 1.61) during jogging. In the swing phase, the OUB increased proximal-distal translational ROM by 12.64 mm (ES = 1.31) during walking and by 9.23 mm (ES = 0.92) during jogging, decreased abduction-adduction rotational ROM by 2.83°(ES = 0.54) during walking and by 3.37°(ES = 0.67) during jogging, and decreased internal-external rotational ROM by 2.71°(ES = 0.68) during jogging. ConclusionsOUB use increased proximal-distal translation while reducing abduction-adduction rotation. This effect may increase the joint gap of the tibiofemoral joint, thereby reducing joint stress, and may contribute to disease rehabilitation in the knee of clinical orthopedics, rehabilitation, and sports medicine fields. However, additional studies are needed to assess the range of possible clinical and prophylactic benefits of OUB.

Real-Time Robotic Mirrored Behavior of Facial Expressions and Head Motions Based on Lightweight Networks

The ability of a humanoid robot to imitate facial expressions with simultaneous head motions is crucial to natural human–robot interaction. This mirrored behavior from human beings to humanoid robots has high demands of similarity and real-time performance. To fulfill these needs, this article proposes a real-time robotic mirrored behavior of facial expressions and head motions based on lightweight networks. First, a humanoid robot that can change the state of its facial organs and neck through servo displacement is developed to achieve the mirrored behavior of facial expressions and head motions. Second, to overcome the high latency caused by deep learning models running in embedded devices, a lightweight deep learning network is constructed for detecting facial feature points, which can reduce model size and improve running speed without affecting the performance of the model. Finally, a mapping relationship of 68 facial feature points to optimal servo displacements is established to realize the mirrored behavior from human beings to humanoid robots. The experimental results show that the facial feature point recognition method based on the lightweight model performs better than other state-of-the-art methods, and our head motion tracking method can maintain high accuracy compared with the gold standard optical motion capture system NOKOV. Overall, our method ensures the accurate and real-time generation of robot mirrored behavior and has a certain reference value for the efficient and natural interaction between humans and robots.

Trunk range of motion: A wearable sensor-based test protocol and indicator of fall risk in older people

Conventionally, trunk range of motion (TROM) requires manual measurement by an external health professional with a general-purpose goniometer. This study aims to propose a convenient test protocol to assess TROM based on a single wearable sensor and to further investigate the relationship between TROM and fall risk of older people. We first explored the optimal sensor position by comparing TROMs from four representative locations (T1, T12, L5 and sternum) and optical motion capture system (golden reference). A follow-up experiment was conducted to evaluate the relationship between TROM and fall risk. The results showed that T12 achieved the minimum root mean square error (3.8 ± 2.2°) against the golden reference and the non-faller group had significantly higher TROMs than the faller group. These findings suggest that the newly proposed protocol is convenient yet valid and TROM can be a promising indicator of fall risk in older people.

Leg Muscle Activity and Joint Motion during Balance Exercise Using a Newly Developed Weight-Shifting-Based Robot Control System.

A novel and fun exercise robot (LOCOBOT) was developed to improve balance ability. This system can control a spherical robot on a floor by changing the center of pressure (COP) based on weight-shifting on a board. The present study evaluated leg muscle activity and joint motion during LOCOBOT exercise and compared the muscle activity with walking and sit-to-stand movement. This study included 10 healthy male adults (age: 23.0 ± 0.9 years) and examined basic LOCOBOT exercises (front-back, left-right, 8-turn, and bowling). Electromyography during each exercise recorded 13 right leg muscle activities. Muscle activity was represented as the percentage maximal voluntary isometric contraction (%MVIC). Additionally, the joint motion was simultaneously measured using an optical motion capture system. The mean %MVIC differed among LOCOBOT exercises, especially in ankle joint muscles. The ankle joint was primarily used for robot control. The mean %MVIC of the 8-turn exercise was equivalent to that of walking in the tibialis anterior, and the ankle plantar flexors were significantly higher than those in the sit-to-stand motion. Participants control the robot by ankle strategy. This robot exercise can efficiently train the ankle joint muscles, which would improve ankle joint stability.