Accelerometer Readings Research Articles

A review of the literature on sedentary behavior

Sedentary behavior (such as sitting, watching TV, and driving) is characterized by little physical movement and low energy expenditure (≤1.5 metabolic equivalents). Previous studies have shown associations between sedentary behavior and adverse outcomes, including cancer, cardiovascular disease, and death. This study is a narrative review that aims to summarize the assessment tools for sedentary behavior as well as the association between sedentary behavior and adverse health outcomes. Additionally, guidelines for future research on sedentary behavior are discussed.Sedentary behavior is assessed using subjective methods such as questionnaire responses or objective methods such as accelerometer readings. Although questionnaire methods have low validity, they allow researchers to collect information regarding the purpose or occasion of physical activity at a low cost. Accelerometers are expensive but collect precise information on the duration of sedentary behavior. Sedentary behavior is a risk factor for the incidence of cardiovascular diseases and cancer and associated mortality. The association between sedentary behavior and some diseases such as cancer type have been reported. Previous studies have found a dose-response relationship between sedentary time and adverse outcomes. Research on the associations between sedentary behavior and the subtypes of stroke, heart disease, and orthopedic diseases is insufficient; further studies are needed to clarify these associations. Recently, sedentary behavior has been the focus of guidelines in some countries because of increasing evidence on its adverse consequences. Intervention studies have shown that taking breaks and replacing sedentary behavior with vigorous or high-intensity physical activity benefit the health of participants. Future studies are needed to examine the cutoff values of sitting time that reduce the risk of morbidity for each disease and the health effects of interventions that shorten sitting time.

Using Apple Watches to Monitor Health and Behaviors of Individuals with Cognitive Impairment: A Case Series Study.

This study explores the potential of developing digital biomarkers from wearables for monitoring individuals with Alzheimer's Disease and Related Dementias, focusing on the feasibility of using Apple Watches for tracking health and behaviors in older adults with cognitive impairment. Data collection used the Amissa Health technology stack, which passively collects time-series data from smartwatches and provides a high-frequency cloud database for secure data storage, query, and visualization by clinicians and researchers. The platform consists of i) AmissaWear, a software app that runs on smartwatches and sends information to a cloud database using a secure API; and ii) AmissaOrbis, a centralized cloud portal for the collected data. Each participant was provided an Apple Watch configured to collect steps, calories burned, accelerometer and gyroscope readings, heart rate, and sleep information. Seven participants, with cognitive impairment diagnosed by a neurologist, were enrolled in the study from December 2023 through June 2024. The watches successfully collected more than 700,000 observations during the study. Each observation contains data recorded from over a dozen sensors (e.g., heart rate, pedometer, gyroscope, accelerometer). The participants wore Apple Watches for an average of 11.48 hours/day for 84.91% of days during a 6-month period without a decrease in usage over time. Overall, the technology yielded high wear adherence and participation within this pilot. This study demonstrates the feasibility of using widely available Apple Watches for continuous monitoring of individuals with cognitive impairment and provides insights into their daily health and activity patterns, which could aid in future development of digital biomarkers.

Technical aspect of the operation of accelerometers as composition of a system for monitoring engineering structures of a cable-stayed bridge over the Petrovsky canal in the construction of the Western Speed Diameter highway in Saint Petersburg

Aim. This study analyzes the current monitoring system for the engineering structures of a cable-stayed bridge across the Petrovsky Canal, part of the Western High-Speed Diameter highway in St. Petersburg, focusing on emergency situations where accelerometers on pylons record values that exceed limits. Methods and Materials. The study utilizes statistical data from existing monitoring system databases. The tasks, set within the framework of the study, are accomplished by applying theoretical scientific methods including analytical techniques, mathematical statistics, and induction. Results. The analysis presents an overview of the current system used for monitoring engineering structures and highlights issues related to emergency situations in which accelerometer readings exceed threshold values. This study proposes a method for assessing the reliability of the monitoring system by classifying the data obtained from the accelerometer into two groups. The research identifies the need for further investigations to understand the root causes of these issues. Conclusion. Тhe results can assist in the design and installation of monitoring systems of the engineering structures for cable-stayed bridges, as well as for modernization and optimization of existing monitoring systems to improve the quality of structural technical condition assessments.

Humanactivity Recognition using Deep Learning

Human activity recognition (HAR) plays a crucial role in various fields, including healthcare, surveillance, and human-computer interaction. This study explores the application of deep learning techniques for accurate and efficient human activity recognition. Leveraging the capabilities of deep neural networks, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the proposed approach aims to capture both spatial and temporal features from sensor data. The dataset utilized in this research comprises multi-model sensor inputs, such as accelerometer and gyroscope readings, collected from wearable devices. The deep learning model is designed to automatically learn hierarchical representations of the raw sensor data, enabling robust feature extraction and discrimination between different human activities. Transfer learning is employed to enhance model generalization across diverse activity categories and varying sensor setups. Experimental evaluations are conducted on real-world datasets, demonstrating the effectiveness of the proposed deep learning framework in accurately classifying activities, including walking, running, sitting, and standing. Comparative analyses against traditional machine learning methods underscore the superior performance and adaptability of deep learning in handling complex and dynamic activity patterns. The results showcase the potential of deploying deep learning models in real-time human activity recognition systems, highlighting their scalability and efficiency. The study contributes to the advancement of HAR methodologies, paving the way for the development of more reliable and robust systems in applications such as healthcare monitoring, assisted living, and smart environments.

The method for calculating phase angle between exciter force of vibration exciter and roller displacement

Introduction. In the process of soil compaction it is important to have information about the current density of the layer, as it enables to quickly adjust the load on the compacted material. The field methods of compaction quality assessment do not cope with this task, as they make point estimation within the pavement area. Therefore, continuous compaction monitoring systems installed on vibratory road rollers are becoming increasingly common. The systems developed by BOMAG and AMMANN require, among other things, the phase angle between the exciter force and the roller movement to calculate the compaction quality index. The phase angle is determined by the unbalance position sensor, which is very labor-intensive. In addition, continuous compaction monitoring systems include an accelerometer. The purpose of this paper is to develop an indirect method for calculating the phase angle from accelerometer readings.The method of research. In order to achieve the purpose of the work, a roller-soil single-mass model in a typical mode for vibratory rollers (periodic loss of contact) has been studied. As a result of modeling it has been found that the reaction of the compacted material has the main influence on the vertical component of a roller acceleration and practically does not affect the horizontal component. This is confirmed by the experimental data.Results. The phase angle can be determined by mutual correlation of the horizontal and vertical acceleration signals of the roller obtained with the accelerometer.Conclusion. The study proposes a new method of calculating the phase angle between the exciter force and the roller displacement, which eliminates the direct measurement of this angle. The calculation of the angle is based on the readings of a two-axis accelerometer installed on the road roller. The proposed method enables to simplify the system of continuous compaction control and reduce the labor intensity of phase angle measurement.

Accident Prediction and Emergency Response System Using Accelerometer Sensor

Accidents, being unforeseeable events, can result in serious consequences, particularly when immediate medical attention is necessary. To address this issue, we propose the development of an Accident Prediction and Emergency Response System (APERS) that utilizes mobile accelerometer sensors. The APERS comprises two key components: a mobile application installed on users' smartphones and a web-based application for hospitals. The mobile application enables users to input and update their personal information and medical records, which are securely encrypted and stored. Moreover, the application continuously monitors accelerometer sensor data in real-time. Upon detecting a sudden spike in accelerometer readings indicative of an accident, the user's encrypted data and medical records are automatically transmitted to the hospital-side web application. On the hospital side, the web-based application receives and decrypts the user data, granting medical professionals access to vital information even if the user is unconscious. This facilitates timely and well-informed medical interventions, potentially saving lives. The system operates seamlessly, providing a robust solution for accident prediction and emergency response. The APERS offers numerous benefits, including proactive accident prediction, automated data sharing, and swift medical response. By leveraging mobile technology and accelerometer sensors, it addresses the pressing need for efficient emergency services in accident scenarios. Looking ahead, users could potentially share data while crossing the reception, further enhancing the system's effectiveness.

Predicting Peak Ground Acceleration of Strong-Motion Earthquakes Using Variable Snapshots of P-Wave Data with Long Short-Term Memory Neural Network

Abstract Conventional earthquake early warning systems (EEWS) rely on mathematical functions that utilize P-wave parameters extracted over a 3 s window to estimate peak ground acceleration (PGA). Advancements in the capabilities of deep neural networks to approximate universal functions, coupled with the availability of strong seismic event data, offer an unprecedented opportunity to evaluate the relationship between variable snapshots of P-wave data and the PGA of strong-motion earthquakes. This convergence of technology and data opens new avenues for research into utilizing smaller snapshots of P-wave in EEWS. Our study centers on the utilization of a long short-term memory (LSTM) neural network to model long dependencies within the P-wave of 1839 earthquakes recorded by the Kiyonshin Network (K-NET) for the prediction of PGA of S waves. Our methodology involves experiments that ultimately evaluate the network’s performance on 4, 3, and 2 s of P-wave snapshots. Our findings indicate that there is sufficient information in 2 s of temporal accelerometer readings after the onset of P waves to predict PGA accurately with an LSTM network.

M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training

Over the years, multimodal mobile sensing has been used extensively for inferences regarding health and well-being, behavior, and context. However, a significant challenge hindering the widespread deployment of such models in real-world scenarios is the issue of distribution shift. This is the phenomenon where the distribution of data in the training set differs from the distribution of data in the real world---the deployment environment. While extensively explored in computer vision and natural language processing, and while prior research in mobile sensing briefly addresses this concern, current work primarily focuses on models dealing with a single modality of data, such as audio or accelerometer readings, and consequently, there is little research on unsupervised domain adaptation when dealing with multimodal sensor data. To address this gap, we did extensive experiments with domain adversarial neural networks (DANN) showing that they can effectively handle distribution shifts in multimodal sensor data. Moreover, we proposed a novel improvement over DANN, called M3BAT, unsupervised domain adaptation for multimodal mobile sensing with multi-branch adversarial training, to account for the multimodality of sensor data during domain adaptation with multiple branches. Through extensive experiments conducted on two multimodal mobile sensing datasets, three inference tasks, and 14 source-target domain pairs, including both regression and classification, we demonstrate that our approach performs effectively on unseen domains. Compared to directly deploying a model trained in the source domain to the target domain, the model shows performance increases up to 12% AUC (area under the receiver operating characteristics curves) on classification tasks, and up to 0.13 MAE (mean absolute error) on regression tasks.

Gestures Controlled Robot

Abstract: The main abstract of the project work is to control robot with gestures using hand. There are two main components of the system: The accelerometer depends upon the gestures of the hand. Through accelerometer, a passage of data signal is received and it is processed with the help of Atmega328 microcontroller. The microcontroller gives command to the robot to move in the desired direction. The basic working principle for the robot is passage of the data signals of accelerometer readings to the Arduino board fitted on the bot. The program compiled in that Atmega328 runs according to that value, which make the bot function accordingly. While we have used three-axis accelerometer. In which, one axis will control the speed in forward or backward direction and other axis will control the turning mechanism. Accelerometer-based gesture control is studied as a supplementary or an alternative interaction modality. Gesture commands freely trainable by the user can be used for controlling external devices with handheld wireless sensor unit.

Techniques for Accelerometer Reading Processing on Railway Transport Using Wavelet Transform

Introduction. Safety issues of railway transport are inevitably connected with the condition of railway tracks and railway wheels. Various defects, such as irregularities of the railway track, may lead to emergencies and incidents. Therefore, it is important to measure and calculate short and impulse irregularities. A joint analysis of vibration acceleration signals is needed in order to study the types and size of railway track irregularities.Aim. Development of an algorithm for irregularity search based on accelerometer readings with the vertical measurement axis.Materials and methods. The research encompassed wavelet transformation and wavelet-based signal processing including discrete-time signal processing and continuous wavelet processing. In addition, time-frequency analysis based on Fourier transform and continuous wavelet scalogram was used. These methods provide for time-frequency localization for irregularity detection and measurement.Results. Algorithms for vibrational signal processing using the continuous and discrete-time wavelet transform are proposed. The results show that the discrete wavelet transform is effective for multiresolution and multiband analysis, and continuous wavelet transform and wavelet scalogram allows extraction of irregularities and determination of their parameters. The relative error for irregularity depth was improved by 18 %, and the absolute error for irregularity length determinations was reduced by 7 times.Conclusion. Application of the discrete Fourier transform and Fourier spectrogram provides for fine resolution in the frequency domain. However, separation of signal components in the time-frequency domain is impeded. The continuous-time wavelet transform ensures sufficient resolution in the low-frequency domain for component localization and visualization of irregularities.

Exploring Upper Limb Kinematics in Limited Vision Conditions: Preliminary Insights from 3D Motion Analysis and IMU Data

This study examined upper limb kinematics under simulated visually impaired conditions. By investigating how upper limb movements adapt in these circumstances, we aimed to gain insights that may prove valuable for both healthy populations and those with clinical conditions. Data, including 3D marker-based motion capture and accelerometer readings, were collected from seven healthy volunteers walking on a treadmill at 1.5 m/s under two conditions: without glasses (NG) and with stroboscopic glasses (G). Each walking condition lasted 2 minutes, and two 10-second recordings were analyzed from the midpoint of each session. Contrary to our hypothesis, simulated visual impairment with stroboscopic eyewear at 6Hz did not statistically affect upper limb kinematics, specifically in terms of wrist acceleration and hand distance in the frontal plane. Future research should explore varied visual impairment conditions, extend task durations, and investigate the relationship between subjective gait difficulties and biomechanical data.

Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data.

In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.

Temperature compensation in high accuracy accelerometers using multi-sensor and machine learning methods

Temperature is a major source of inaccuracy in high-sensitivity accelerometers and gravimeters. Active thermal control systems require power and may not be ideal in some contexts such as airborne or spaceborne applications.We propose a solution that relies on multiple thermometers placed within the accelerometer to measure temperature and thermal gradient variations. Machine Learning algorithms are used to relate the temperatures to their effect on the accelerometer readings. However, obtaining labeled data for training these algorithms can be difficult. Therefore, we also developed a training platform capable of replicating temperature variations in a laboratory setting.Our experiments revealed that thermal gradients had a significant effect on accelerometer readings, emphasizing the importance of multiple thermometers.The proposed method was experimentally tested and revealed a great potential to be extended to other sources of inaccuracy as well as to other types of measuring systems, such as magnetometers or gyroscopes.

MEMS fault-tolerant machine learning algorithm assisted attitude estimation for fixed-wing UAVs

The utilization of Micro-electromechanical Systems (MEMS) sensors is widespread for directly detecting attitude angles, such as Accelerometer, Gyro, and Magnetometer readings. However, these MEMS sensors are prone to flaws, leading to inaccurate estimates of attitude angles and, consequently, causing UAVs to lose control. Given that UAVs are operated remotely over long distances, ensuring accurate attitude estimates becomes crucial. This study aims to address this issue by employing machine learning algorithms (MLA). These algorithms were trained and evaluated to overcome the problem by predicting missing data from a malfunctioning MEMS sensor using the available data from other MEMS sensors. To calculate the attitude angles, the study utilizes the Extended Kalman Filter (EKF) technique. Furthermore, a novel fault-tolerant machine learning-aided estimation algorithm has been proposed specifically for estimating the attitude angles (φˆ, θˆ, ψˆ) of fixed-wing UAVs. The significance of this research becomes even more prominent when considering the occurrence of MEMS sensor failure. In such cases, the machine learning algorithm plays a crucial role as it has been pre-trained specifically to handle these scenarios. The algorithm is equipped with the ability to effectively address and mitigate the challenges posed by MEMS sensor failures. By leveraging its pre-existing knowledge and learned patterns, the algorithm can accurately predict missing data caused by malfunctioning MEMS sensors. This capability proves invaluable in ensuring the reliable estimation of attitude angles, even in the face of sensor failures. Thus, the integration of machine learning into the estimation process enhances the resilience and robustness of the system, making it well-equipped to handle the uncertainties introduced by MEMS sensor failures.

Theoretical Analysis of Composite RC Beams with Pultruded GFRP Beams subjected to Impact Loading

Glass Fiber Reinforced Polymer (GFRP) beams have gained attention due to their promising mechanical properties and potential for structural applications. Combining GFRP core and encasing materials creates a composite beam with superior mechanical properties. This paper describes the testing encased GFRP beams as composite Reinforced Concrete (RC) beams under low-velocity impact load. Theoretical analysis was used with practical results to simulate the tested beams' behavior and predict the generated energies during the impact loading. The impact response was investigated using repeated drops of 42.5 kg falling mass from various heights. An analysis was performed using accelerometer readings to calculate the generalized inertial load. The integrated acceleration record and the measured hammer load vs. time data were utilized to determine the generalized bending load and fracture energy. Four forms of energy were calculated at the maximum load. The total energy was calculated and divided into two parts: The first part was gained by the beam's rotational kinetic energy, the bending energy in the specimen, and the elastic strain energy. The second part was the hammer's kinetic energy before striking the beam. The analytical results showed that the bending energy was less than its rotational kinetic energy for the encased GFRP beams and the reference specimens. In contrast, the encased steel beams had high bending energy due to the higher impact load and deflection. Strain energy recorded lower energy values for all specimens with higher bending energy. There is a good agreement between the tested and the calculated inertial and bending force for all beams. The ratio of inertia force to the total impact load for the encased GFRP and encased steel beams to the reference beam is about 9% and 5%, respectively.

Detecting Parkinson’s Disease Using Machine Learning from Movement

This study explored the potential of machine learning techniques, specifically decision trees and artificial neural networks (ANNs), to detect Parkinson's Disease (PD) using data collected from the mPower mobile iOS app. Released in 2016 by Sage Bionetworks, mPower enables individuals, both with and without PD, to assess their cognitive and physical abilities through various tasks related to memory, tapping, voice, and movement. The main focus of this study is on the walking task within the app's version 1.0 build 7. Participants were required to walk unassisted for approximately 20 steps in a straight line, followed by a 30-second period of standing still, and then returning with 20 steps. The smartphone's accelerometer and gyroscope captured three-dimensional (3D) rotation data (x, y, z) during these movements, with the device placed in the participant's pocket or bag. A convolutional neural network was applied to the movement dataset to assess confirmed PD cases, utilizing accelerometer and gyroscope readings during outward walking, return walking, and rest periods.

Evaluation of Elderly Fall Detection Systems using Data Analysis

As the global population continues to age, it is more important to ensure the safety and wellbeing of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries. Modern technology, such as the Elderly Fall Detection System, have been created to solve this issue. This technology attempts to detect falls quickly and react to them, offering aid right away and maybe saving lives. These studies identify a fall when the tri-axial accelerometer reading from a wearable device exceeds the predetermined threshold. Less complexity and computational expense compared to other approaches is one of the main benefits of adopting threshold-based methods. Finding the right threshold value, however, to accurately identify all falls without confounding them with certain ADL, has proven to be a challenging issue. Early Intervention and Medical Aid: Being independent and able to live in one's own house are two things that many elderly people enjoy. Support for carers and peace of mind: detection systems not only aid the elderly but also provide their carers peace of mind. Cost-cutting: Falls among the elderly frequently lead to hospital stays, rehab, and higher healthcare expenses. The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors. It is an easy and obvious method that enables decision-makers to weigh the relative weight of many variables and make wise decisions. Smart surveillance system, Smart cane, Smart Carpet, Smart phone/watch and app. Unobtrusiveness, reliability, privacy, Cost. As the global population continues to age, it is more important to ensure the safety and wellbeing of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors Unobtrusiveness, reliability, privacy, Cost, smart phone is the highest and smart carpet is the lowest

Estimation of cardiorespiratory fitness using heart rate and step count data

Predicting cardiorespiratory fitness levels can be useful for measuring progress in an exercise program as well as for stratifying cardiovascular risk in asymptomatic adults. This study proposes a model to predict fitness level in terms of maximal oxygen uptake using anthropometric, heart rate, and step count data. The model was trained on a diverse cohort of 3115 healthy subjects (1035 women and 2080 men) aged 42 ± 10.6 years and tested on a cohort of 779 healthy subjects (260 women and 519 men) aged 42 ± 10.18 years. The developed model is capable of making accurate and reliable predictions with the average test set error of 3.946 ml/kg/min. The maximal oxygen uptake labels were obtained using wearable devices (Apple Watch and Garmin) during recorded workout sessions. Additionally, the model was validated on a sample of 10 subjects with maximal oxygen uptake determined directly using a treadmill protocol in a laboratory setting and showed an error of 4.982 ml/kg/min. Unlike most other models, which use accelerometer readings as additional input data, the proposed model relies solely on heart rate and step counts—data readily available on the majority of fitness trackers. The proposed model provides a point estimation and a probabilistic prediction of cardiorespiratory fitness level, thus it can estimate the prediction’s uncertainty and construct confidence intervals.

Instrumentation of Field-Testing Sites for Dynamic Characterization of the Temperature-Dependent Stiffness of Pavements and Their Layers

Falling weight deflectometer (FWD) tests are performed worldwide for assessing the health of pavement structures. Interpretation of FWD-measured surface deflections turns out to be challenging because the behavior of pavement structures is temperature-dependent. In order to investigate the influence of temperature on the overall pavement performance and on the stiffness of individual layers, temperature sensors, asphalt strain gauges, and accelerometers were installed into one rigid (concrete) and two flexible (asphalt) pavement structures, mostly at layer interfaces. Three different methods for installation of the strain gauges are compared. From correspondingly gained experience, it is recommended to install a steel dummy as a place-holder into the surface of hot asphalt layers, immediately after their construction and right before their compaction, and to replace the dummy with the actual sensor right before the installation of the next layer. Concerning the first data obtained from dynamic testing at the field-testing sites, FWD tests performed at different temperatures deliver, as expected, different surface deflections. As for the rigid pavement, sledgehammer strokes onto a metal plate, transmitted to the pavement via a rubber pad, yield accelerometer readings that allow for detection of curling (=temperature-gradient-induced partial loss of contact of the concrete slab from lower layers). In the absence of curling, the here-proposed sledgehammer tests yield accelerometer readings that allow for quantification of the runtime of longitudinal waves through asphalt, cement-stabilized, and unbound layers, such that their stiffness can be quantified using the theory of elastic wave propagation through isotropic media.

Shoulder Range of Motion Measurement Using Inertial Measurement Unit—Validation with a Robot Arm

The invention of inertial measurement units allowed the construction of sensors suitable for human motion tracking that are more affordable than expensive optical motion capture systems, but there are a few factors influencing their accuracy, such as the calibration methods and the fusion algorithms used to translate sensor readings into angles. The main purpose of this study was to test the accuracy of a single RSQ Motion sensor in comparison to a highly precise industrial robot. The secondary objectives were to test how the type of sensor calibration affects its accuracy and whether the time and magnitude of the tested angle have an impact on the sensor’s accuracy. We performed sensor tests for nine repetitions of nine static angles made by the robot arm in eleven series. The chosen robot movements mimicked shoulder movements in a range of motion test (flexion, abduction, and rotation). The RSQ Motion sensor appeared to be very accurate, with a root-mean-square error below 0.15°. Furthermore, we found a moderate-to-strong correlation between the sensor error and the magnitude of the measured angle but only for the sensor calibrated with the gyroscope and accelerometer readings. Although the high accuracy of the RSQ Motion sensors was demonstrated in this paper, they require further study on human subjects and comparisons to the other devices known as the gold standards in orthopedics.