Standard Deviation Of Acceleration Research Articles

Factors Affecting Rear-End Collisions in Underground Road Junctions Using VISSIM

Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high risk of accidents compared to surface roads. In particular, there is limited study on junctions that connect two or more underground roads. In this study, an underground road network including junctions was constructed to analyze the factors behind rear-end collisions at underground road connections. To reflect the driving behavior on underground roads, the scenario analysis was conducted by applying the speed distribution of underground roads in Korea. The results of the analysis showed that variables such as acceleration standard deviation and lateral position standard deviation are crucial for accidents on underground roads. Thus, this study can be used as a basis for traffic management and safety improvement in the operation of underground road junctions in the future.

Surrogate-assisted optimization of floating wind turbine substructure

This paper presents a surrogate-assisted optimisation approach to speed up the substructure analysis in the preliminary design phase. The approach consists of replacing the radiation-diffraction analysis in a frequency domain analysis model for floating wind turbines with a data-driven surrogate model predicting the hydrodynamic coefficients for parameterised substructure geometries. This procedure is compared with the reference approach of estimating the hydrodynamic coefficients via radiation-diffraction analysis. A representative use case of assessing the trade-off between minimising the capital cost and reducing the wave-induced nacelle acceleration standard deviation for a semi-submersible substructure is presented. The accuracy of the surrogate model is found to increase significantly up to training datasets consisting of 400 designs and less noticeably afterwards. For a dataset consisting of 400 designs, the mean error on the prediction of the hydrodynamic coefficients and the error at one standard deviation from the mean are generally below 7% and 10%, respectively. For the same dataset size, the mean error on the most probable maximum wave-induced pitch over a 3h storm period is below 17%, while the error at one standard deviation from the mean is lower than 27%. The same values for the most probable maximum nacelle acceleration are under 7% and 12%, respectively. The surrogate model can capture the trade-off between the two objective functions, and the optimal designs identified with the surrogate model generally follow the same trend as those obtained with the reference model. However, relying on the surrogate model for performing the analysis of the substructure introduces local minima in the objective function that cause a discrepancy between the optimal designs identified with the surrogate model and those identified with the reference model.

Revealing the impact of stochastic driving characteristics on car-following behavior with locally collected vehicle trajectory data

This study investigates the impact of Chinese drivers’ stochastic behaviour on local car-following situations using localized trajectory data. An extended stochastic car-following model (S-IDM) is established, which considers both internal and external stochasticity. External stochasticity is characterized by different driver types, while internal stochasticity is characterized by the standard deviation of acceleration under different headway and velocity differences for the same driver. The proposed model shows advantages in terms of single-vehicle simulation accuracy and traffic shock reproduction ability, compared to traditional and existing car-following models. The model can also be extended to the evolution analysis of mixed traffic flow models, where reducing the stochasticity of human-driven vehicles is critical for optimizing and controlling traffic flow.

Assessing the Effects of Modalities of Takeover Request, Lead Time of Takeover Request, and Traffic Conditions on Takeover Performance in Conditionally Automated Driving

When a conditionally automated vehicle controlled by the machine faces situations beyond the capability of the machine, the human driver is requested to take over the vehicle. This study aims to assess the short-term effects of three factors on the takeover performance: (1) traffic conditions (complex and simple); (2) modality of takeover request (auditory and auditory + visual); (3) lead time of takeover request (TORlt, 5 s and 7 s). The scenario is the obstacle ahead. Indicators include: (1) Take Over Reaction Time (TOrt); (2) approximate entropy (ApEn), operating order of steering wheel Angle and pedal torque; (3) the choice of target lane and speed of lane-changing; (4) mean and standard deviation of acceleration and velocity; (5) quantifiable lateral cross-border risk and longitudinal collision risk. A driving simulation experiment is conducted to collect data for analysis. The effects of the three factors on takeover performance are analyzed by analysis of variance (ANOVA) and non-parametric tests. The results show that when the traffic conditions are complex, drivers have a larger ApEn of the steering wheel angle and brake pedal torque, and a smaller ApEn of acceleration pedal torque. In the 5 s TORlt case, drivers have a smaller ApEn of brake pedal torque the interaction between TORlt, traffic conditions, and modality of TOR affects ApEn of accelerator pedal torque. 5 s TORlt/complex traffic condition makes the scene more urgent, which is easy to cause driver to make sudden and simultaneous turning and sudden braking dangerous behavior meanwhile. Compared with other combinations of modality and TORlt, the combination of 7 s and auditory + visual significantly reduces the lateral cross-border risk and longitudinal collision risk.

Pareto Optimal Information Flow Topology for Control of Connected Autonomous Vehicles

Information flow topology plays a crucial role in the control of connected autonomous vehicles. This paper proposes an approach to search for the Pareto optimal information flow topology off-line for the control of connected vehicles’ platoon using a non-dominated sorting genetic algorithm. Based on the obtained Pareto optimal information flow topology, the platoon's overall performance in terms of three main performance indices: tracking index, acceleration standard deviation, and fuel consumption, are all improved. Numerical simulations are used to validate the effectiveness of the proposed approach. In the simulation, the impact of different information flow topologies on the performance of the connected autonomous vehicles platoon is firstly investigated. The results show that more communication links can lead to better tracking ability. The smoothness of the velocity profile is consistent with fuel economy, while velocity profile's smoothness, fuel economy and communication efficiency are in contrary to the tracking index. Then, five cases are discussed using the Pareto optimal information flow topology. The results indicate that while ensuring the platoon's stability, the obtained Pareto optimal information flow topology can improve the tracking ability by 33.67% to 49.35%, and fuel economy by 7.181% to 16.93% and driving comfort up to 14.9%.

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.

Multi-Objective Asymmetric Sliding Mode Control of Connected Autonomous Vehicles

The platoon of connected autonomous vehicles plays an essential role in future intelligent transportation. It can improve traffic efficiency and release traffic congestion. However, there are lots of existing challenging problems of the control of connected autonomous vehicles, such as the negative impact caused by wireless communication and disturbance. To solve these challenges, a multi-objective asymmetric sliding mode control strategy is proposed in this paper. Firstly, the asymmetric degree is introduced in the topological matrix. Then, a sliding mode controller is designed targeting platoon’s tracking performance. Moreover, Lyapunov analysis are used via Riccati inequality to find the controller’s gains and guarantee internal stability and Input-to-output string stability. Finally, a non-dominated sorting genetic algorithm is utilized to find the Pareto optimal asymmetric degree regarding the overall performance of the platoon, including tracking index, fuel consumption, and acceleration standard deviation. Four different information flow topologies, including a random topology are studied. The results indicate that the proposed asymmetric sliding mode controller can ensure platoon’s stability while improving its performance. The tracking ability is improved by 54.61% and 75.17%, fuel economy is improved by 0.78% and 6.34% under the Urban Road and Highway Case Study, respectively.

Uncertainty Analysis and Optimization of Quasi-Zero Stiffness Air Suspension Based on Polynomial Chaos Method

To improve the vibration isolation performance of suspensions, various new structural forms of suspensions have been proposed. However, there is uncertainty in these new structure suspensions, so the deterministic research cannot reflect the performance of the suspension under actual operating conditions. In this paper, a quasi-zero stiffness isolator is used in automotive suspensions to form a new suspension−quasi-zero stiffness air suspension (QZSAS). Due to the strong nonlinearity and structural complexity of quasi-zero stiffness suspensions, changes in structural parameters may cause dramatic changes in suspension performance, so it is of practical importance to study the effect of structural parameter uncertainty on the suspension performance. In order to solve this problem, three suspension structural parameters d0, L0 and Pc0 are selected as random variables, and the polynomial chaos expansion (PCE) theory is used to solve the suspension performance parameters. The sensitivity of the performance parameters to different structural parameters was discussed and analyzed in the frequency domain. Furthermore, a multi-objective optimization of the structural parameters d0, L0 and Pc0 of QZSAS was performed with the mean and variance of the root-mean-square (RMS) acceleration values as the optimization objectives. The optimization results show that there is an improvement of about 8%−10% in the mean value and about 40%−55% in the standard deviation of acceleration (RMS) values. This paper verifies the feasibility of the PCE method for solving the uncertainty problem of complex nonlinear systems, which provide a reference for the future structural design and optimization of such suspension systems.

Detection of Food Intake Sensor's Wear Compliance in Free-Living.

Objective detection of periods of wear and non-wear is critical for human studies that rely on information from wearable sensors, such as food intake sensors. In this paper, we present a novel method of compliance detection on the example of the Automatic Ingestion Monitor v2 (AIM-2) sensor, containing a tri-axial accelerometer, a still camera, and a chewing sensor. The method was developed and validated using data from a study of 30 participants aged 18-39, each wearing the AIM-2 for two days (a day in pseudo-free-living and a day in free-living). Four types of wear compliance were analyzed: 'normal-wear', 'non-compliant-wear', 'non-wear-carried', and 'non-wear-stationary'. The ground truth of those four types of compliance was obtained by reviewing the images of the egocentric camera. The features for compliance detection were the standard deviation of acceleration, average pitch, and roll angles, and mean square error of two consecutive images. These were used to train three random forest classifiers 1) accelerometer-based, 2) image-based, and 3) combined accelerometer and image-based. Satisfactory wear compliance measurement accuracy was obtained using the combined classifier (89.24%) on leave one subject out cross-validation. The average duration of compliant wear in the study was 9h with a standard deviation of 2h or 70.96% of total on-time. This method can be used to calculate the wear and non-wear time of AIM-2, and potentially be extended to other devices. The study also included assessments of sensor burden and privacy concerns. The survey results suggest recommendations that may be used to increase wear compliance.

Main characteristic parameters to describe driving patterns and construct driving cycles

• A database of trips was built by monitoring the regular operation of 15 diesel buses. • 1140 combinations of CPs were used as assessment parameters of representativeness. • The MT method was replicated 1000 times for each combination to observe tendencies. • Avg. speed, % idling time, and acceleration’s Std Dev were identified as the main CPs. • The use of these CPs in the MT method assures the representativeness of the DCs. At present, it is unknown which set of characteristic parameters (CP i ) should be used as assessment criteria in the micro-trips method to construct driving cycles that truly represent local driving patterns and reproduce the real energy consumption and tailpipe emissions of electric and engine-powered vehicles when they are tested on a chassis dynamometer . Using 1-Hz simultaneous measurements of energy consumption, speed, and emissions from the regular operation of a vehicle's fleet, we constructed driving cycles using 1140 different combinations of CP i as assessment parameters. We reproduced the process a thousand times for each combination and observed the tendency and the dispersion of the degree of representativeness of the resulting driving cycles. We concluded that mean speed, percentage of idling time, and the standard deviation of acceleration are the CP i that must be included in the micro-trips method.

Assessment of pavement structure using multi-source data from a moving vehicle

In this paper we propose a novel method for assessing pavement structure based on multi-source data from a moving vehicle. The classification of pavement on subgrade (POS) and pavement on bridge deck (PBD) was used as examples. A mobile acquisition system was designed to collect multi-source data. Vehicle accelerations on PBD and POS were analyzed, and some features were selected to build 15 Support Vector Machine classifiers. Our results show that some features are helpful, including vehicle speed, maximum value, minimum value, standard deviation of acceleration in the time domain, and three peak frequencies in the frequency domain. The accuracy was 91.67%. Then, some sections were tested with a falling weight deflectometer to verify the structural differences between POS and PBD. Our results provide a novel method for analyzing pavement structure via vehicle vibration, and this work will be built on for more detailed analysis of pavement structure in the future.

Feasibility of Using Commercially Available Accelerometers to Monitor Upper Extremity Home Practice With Persons Post-stroke: A Secondary Data Analysis

Background: Adherence to home practice rehabilitation programs is important for efficacy; however, adherence is challenging for many individuals post-stroke. Accelerometers have emerged as a potential means to support home practice. This secondary data analysis explored the use of a commercially available accelerometer with custom software to collect and analyze data to corroborate self-reported practice collected during a home program.Methods: The initial study was a single subject design trial that investigated the effect of preferred music listening on adherence to an upper extremity home practice program (Trial Number NCT02906956. ClinicalTrials.gov). The participants (n = 7) were post-stroke adults with aphasia and hemiparesis of the upper extremity. Participants completed home program exercises while wearing accelerometers and recorded practice times in a logbook. Data were collected, cleaned, processed, and analyzed to facilitate descriptive comparisons and clinical interpretations of accelerometer output data.Results: Across all participants, an average of 47% of data were captured and usable for analysis. Five out of seven participants self-reported longer practice times compared to accelerometer duration output by a mean of 66.5 s. Individual exercise set mean total angular velocity and standard deviation of acceleration demonstrated potential for use across time to monitor change.Conclusions: One challenge of integrating accelerometers into clinical practice is the amount of data loss and the steps for data processing. The comparisons of available accelerometer data to the self-reported logs, however, were generally representative. Future investigations should explore ways to increase data capture and accessibility of the data for feedback to the client and practitioner.

Validity of a Non-Proprietary Algorithm for Identifying Lying Down Using Raw Data from Thigh-Worn Triaxial Accelerometers.

Body postural allocation during daily life is important for health, and can be assessed with thigh-worn accelerometers. An algorithm based on sedentary bouts from the proprietary ActivePAL software can detect lying down from a single thigh-worn accelerometer using rotations of the thigh. However, it is not usable across brands of accelerometers. This algorithm has the potential to be refined. Aim: To refine and assess the validity of an algorithm to detect lying down from raw data of thigh-worn accelerometers. Axivity-AX3 accelerometers were placed on the thigh and upper back (reference) on adults in a development dataset (n = 50) and a validation dataset (n = 47) for 7 days. Sedentary time from the open Acti4-algorithm was used as input to the algorithm. In addition to the thigh-rotation criterion in the existing algorithm, two criteria based on standard deviation of acceleration and a time duration criterion of sedentary bouts were added. The mean difference (95% agreement-limits) between the total identified lying time/day, between the refined algorithm and the reference was +2.9 (−135,141) min in the development dataset and +6.5 (−145,159) min in the validation dataset. The refined algorithm can be used to estimate lying time in studies using different accelerometer brands.

Experimental research on the effectiveness of navigation prompt messages based on a driving simulator: a case study

In China, F-type-5 m intersections are not uncommon. One approach of these intersections usually includes a driveway closely followed by an intersecting street, and the driveway and the intersecting street are parallel and approximately 5 m apart. Nowadays, drivers often rely on the navigation systems for directions. However, it is found that the navigation systems sometimes mislead or confuse drivers to make wrong turns or miss their turns at such F-type-5 m intersections. This study proposed to employ driving simulation to identify the appropriate prompt message delivered at the right prompt timing to help drivers navigate through such F-type-5 m intersections. First, a within-subjects two-factor experiment was designed. One factor was the Prompt Timing Mode (PTM), representing a set of three sequential messages broadcast by the navigation system at varying distances to the intended intersection; the other factor was the Prompt Message Type (PMT), representing various sets of three sequential messages broadcast by the navigation system. Three Prompt Timing Modes were used: PTM1 = {− 400 m, -200 m, − 30 m}, PTM2 = {− 300 m, − 150 m, − 30 m}, and PTM3 = {− 200 m, − 100 m, − 30 m}. Three Prompt Message Types were defined: PMT-A = {Turn right at the traffic light XXm ahead; Turn right at the traffic light XXm ahead; Turn right}, PMT-B = {Turn right at the traffic light XXm ahead, enter YY street; Turn right at the traffic light XXm ahead, enter YY street; Turn right}, PMT-C = {Turn right at the traffic light XXm ahead, enter YY street, and please use the second right turn lane; Turn right at the traffic light XXm ahead, enter YY street, and please use the second right turn lane; Turn right}. The combinations of the two factors generated nine experimental intersections which were randomly assigned to three experimental routes. Then, a total of 37 drivers were recruited, and participated in the driving simulation experiment from which vehicle operation data were collected under different prompt timing modes and message types. Next, the repeated Analysis of Variance (rANOVA) was performed to examine the effects of different prompt timing modes and prompt message types on vehicle operation indicators, such as Driving Time, Standard Deviation of Speed, Absolute Value of Acceleration, and Standard Deviation of Acceleration. Finally, the grey near-optimal method was adopted to evaluate the effectiveness of three prompt message types under each prompt timing mode. The rANOVA results showed the vehicle operation in the F-type-5 m intersection was affected by prompt timing modes and prompt message types; the evaluation results indicated that PMT-C made drivers perform better in PTM1 and PTM3, while PMT-B made drivers perform better inPTM2. However, the effectiveness of PMT-A was the lowest in each prompt timing mode. The research results provide valuable guidance to design the human machine interface of navigation systems, which can help drivers safely navigate through F-type-5 m intersections. This research also has laid solid foundations for establishing navigation messaging design guidelines.

Analysing vibration environment of a power battery in a running electric vehicle

Power batteries in electric vehicles are subjected to vibration during vehicle's driving. Clarifying battery vibration characteristics in service conditions is the basis for understanding battery performance and for structure improvement. The acceleration responses at multiple points on a battery pack are measured on various road conditions in this study. Indicators, namely, the standard deviation of acceleration root-mean-square (SD-ARMS) and fatigue damage spectrum (FDS), are introduced to analyse vibration differences among various positions and with vibration test standards. Time-forced vibration test profiles are derived based on the calculated FDS curves. The findings are first to indicate obvious vibration inconsistency among the sampling points in the Z direction, with high SD-ARMS values. The fatigue damage values computed from measured data and from standard vibration profiles significantly deviate. This research provides an intact method from measurements, and evaluates vibration levels to generate time-forced vibration profiles for battery durability assessment.

Influence of the tunnel retro-reflective arch in freeway tunnels on driving behavior: A driving simulator study

In this study, a driving simulator experiment was conducted to investigate the influence of tunnel retro-reflective arch (TA) on driving behavior, through the employment and analysis of TAs with disparate spacing alternatives (no TAs, TAs with a spacing of 200 m, and TAs with a spacing of 300 m) and tunnel lengths (800 m, 1800 m, 3500 m, and 5800 m). The performances of a total of 32 drivers were collected and assessed. The tunnels and TAs were accurately replicated in the driving simulator. On the basis of the experimental data, four behavioral variables, namely, average speed, the standard deviation of speed, the standard deviation of acceleration, and lateral position were obtained. A series of linear mixed models with random effects were estimated to reveal the contributing factors of driving behavior subjected to TAs variation. The results demonstrate that drivers showed significantly higher consciousness of speed control and lower mental stress, coupled with less risky driving behavior, under the condition of TAs in long and extra-long tunnels. Furthermore, drivers are more comfortable subjected to TAs with a spacing of 300 m.

Learning Trajectory Patterns via Canonical Correlation Analysis

A substantial body of research has been devoted to the analysis of motion trajectories. Usually, a motion trajectory consists of a set of coordinates, which is called a raw trajectory. In this paper, the authors first use vectors for some artificially constructed global features, such as the mean discrete curvature and standard deviation of acceleration, to represent the raw trajectory data, and then apply a multiset canonical correlation analysis method to extract latent features from the artificially constructed features. The performance of the latent features is then measured by evaluating the accuracy and F1 score of a gradient boosting decision tree model for different datasets, which include paired sample datasets and unpaired sample datasets. The experimental results show that the classifier performance for MCCA features is much better than that obtained for the artificially constructed features, such as that for the motion distance or mean velocity.

Application of PCA-K-means++ combination model to construction of light vehicle driving conditions in intelligent traffic

The construction of typical driving condition of vehicles in line with the actual road traffic conditions in China requires the selection of the same vehicle for two months to collect driving data and the obtention of 496000 driving condition data of light vehicles. The sample data are preprocessed by using multivariate statistical theory and MATLAB. After the elimination of abnormal data, the effective data are extracted before being divided into 3020 kinematic segments. Then, it takes a principal component analysis to reduce the dimension of the characteristic parameter matrix. Through K-means++ clustering algorithm, the six principal components obtained by principal component analysis are clustered into two categories. Then the typical kinematic segments are selected from various fragment libraries by using correlation coefficient method, so as to construct a typical driving condition of the vehicles in a certain city. With the application of PCA-K-means and PCA-K-means++ clustering algorithm, a driving condition curve with a duration of 1200s is constructed before its effectiveness and accuracy being compared and analyzed. The results show that the error rate of driving condition between sample data and driving condition constructed by PCA-K-mean++ clustering algorithm is less than 6 % and the error rate of average speed and acceleration standard deviation is less than 1 %. The correlation degree between working condition curve constructed by PCA-K-means ++ clustering algorithm and sample data is increased by 4.08 %. The proportion of deceleration time and idle time in vehicle driving state is obviously different, which indicates that PCA-K-means++ is a better way to solve the problem and the clustering algorithm can effectively construct the driving condition curve of light vehicles suitable for local cities.

A Method To Estimate Free-living Behaviors Using High-frequency Wrist Accelerometer Data

PURPOSE: To develop a novel method to estimate free-living sedentary behavior and activity intensity using high-frequency wrist accelerometer data. METHODS: Forty-nine participants (mean ± SD; age: 20.4±1.3 yrs, 45.8% male) completed four, 1-hour sessions of free-living behaviors in home, school, community, and exercise environments. Each session was video-recorded (DO) and participants wore an ActiGraph wGT3X-BT (AG) accelerometer on the non-dominant wrist. Videos were coded for whole body movement, contextual activity type, and activity intensity from the Compendium of Physical Activities (e.g. walking, shopping, 2.8 METs). The novel two-step method (SojWrist) first segments AG data into bouts, or “sojourns”, of inactivity (i.e. sedentary and standing behaviors) or activity using an acceleration standard deviation threshold and random forest model. The second step estimates the intensity of inactive (sedentary, light) and active (light, moderate, vigorous) sojourns. Separate inactive and active sojourn RF models were fit to estimate intensity using bout duration and time- and frequency-domain AG signal characteristics. A 90-10 sample split was used for SojWrist development (N = 44) and cross-validation (N=5). Percent agreement between DO and SojWrist was evaluated at each step using second-by-second data. RESULTS: In the cross-validation sample, 91.8% [95%CI: 87.0%, 96.5%] of inactive and active periods were classified correctly from step 1 of SojWrist. After step 2, overall percent agreement between DO and SojWrist was 86.9% [95%CI: 78.9%, 95.0%] across all intensity categories (Table). CONCLUSION: The new SojWrist performed well at estimating free-living activity intensity categories from a wrist worn accelerometer. Future work should strive to improve method performance for predicting activity intensity categories and test validity on a diverse, independent, free-living sample. Supported by NIH NIDDK 1R01DK110148

EQuilibrium: A Prototype of a Sensor-Based Balance Training and Monitoring System

Abstract Integrated sensor devices are becoming a valuable tool for monitoring, evaluation, and real-time feedback during various exercises in sports, physical rehabilitation, and injury prevention. In this paper, we present the design and development of an inertial sensor system integrated into a fitness ball called eQuilibrium. The main application of this system would be balance training, analysis, and monitoring. The sensor system is composed of an inertial measurement unit sensor with a microcontroller and a WiFi communication module. The wireless sensor device is sending data to LabView application running on a laptop computer. The gyroscope and accelerometer provide a variety of signals, and we defined and evaluated three possible metrics to match the signals with the actual execution of the exercises. The metrics include i) angle rotation range, ii) mean absolute angular velocity, mean absolute acceleration deviation, and iv) mean absolute acceleration gradient. For the initial evaluation of the system, we specified test protocol and tasks that were executed by test subjects and monitored by a trained tester. There are four tasks in the protocol, and each task was being executed for at least 5 seconds. The first task was a reference task, standing on the feet of both legs. In the remaining tasks, the subject was standing on the tips of the toes (both legs, single leg), with the heels as high as possible off the ground. We conducted initial tests to analyze the operation of the system and obtain the initial measurement results. Our preliminary results indicate that several scalar metrics derived from motion sensor data can be used to evaluate motion intensity in the control of body balance. Rotation angle range, mean absolute rotation speed, mean absolute acceleration standard deviation, and mean absolute acceleration gradient are scalar values and independent of sensor axis orientation. In the future, we plan to perform more test executions and compare the sensor results with subjective self-reports of the test subjects, as well as the physical rehabilitation expert evaluations.