Commercial Inertial Measurement Unit Research Articles

Validation of LEOMO inertial measurement unit sensors with marker-based three-dimensional motion capture during maximum sprinting in track cyclists

ABSTRACT LEOMO™ is a commercial inertial measurement unit system that provides cycling-specific motion performance indicators (MPIs) and offers a mobile solution for monitoring cyclists. We aimed to validate the LEOMO sensors during sprint cycling using gold-standard marker-based three-dimensional (3D) motion technology (Qualisys, AB). Our secondary aim was to explore the relationship between peak power during sprints and MPIs. Seventeen elite track cyclists performed 3 × 15s seated start maximum efforts on a cycle ergometer. Based on intraclass correlation coefficient (ICC3,1), the MPIs derived from 3D and LEOMO showed moderate agreement (0.50 < 0.75) for the right foot angular range (FAR); left foot angular range first quadrant (FARQ1); right leg angular range (LAR); and mean angle of the pelvis in the sagittal plane. Agreement was poor (ICC < 0.50) between MPIs derived from 3D and LEOMO for the left FAR, right FARQ1, left LAR, and mean range of motion of the pelvis in the frontal and transverse planes. Only one LEOMO-derived (pelvic rotation) and two 3D-derived (right FARQ1 and FAR) MPIs showed large positive significant correlations with peak power. Caution is advised regarding use of the LEOMO for short maximal cycling efforts and derived MPIs to inform peak sprint cycling power production.

Sagittal plane knee kinematics can be measured during activities of daily living following total knee arthroplasty with two IMU.

Knee function is rarely measured objectively during functional tasks following total knee arthroplasty. Inertial measurement units (IMU) can measure knee kinematics and range of motion (ROM) during dynamic activities and offer an easy-to-use system for knee function assessment post total knee arthroplasty. However, IMU must be validated against gold standard three-dimensional optical motion capture systems (OMC) across a range of tasks if they are to see widespread uptake. We computed knee rotations and ROM from commercial IMU sensor measurements during walking, squatting, sit-to-stand, stair ascent, and stair descent in 21 patients one-year post total knee arthroplasty using two methods: direct computation using segment orientations (r_IMU), and an IMU-driven iCloud-based interactive lower limb model (m_IMU). This cross-sectional study compared computed knee angles and ROM to a gold-standard OMC and inverse kinematics method using Pearson's correlation coefficient (R) and root-mean-square-differences (RMSD). The r_IMU and m_IMU methods estimated sagittal plane knee angles with excellent correlation (>0.95) compared to OMC for walking, squatting, sit-to-stand, and stair-ascent, and very good correlation (>0.90) for stair descent. For squatting, sit-to-stand, and walking, the mean RMSD for r_IMU and m_IMU compared to OMC were <4 degrees, < 5 degrees, and <6 degrees, respectively but higher for stair ascent and descent (~12 degrees). Frontal and transverse plane knee kinematics estimated using r_IMU and m_IMU showed poor to moderate correlation compared to OMC. There were no differences in ROM measurements during squatting, sit-to-stand, and walking across the two methods. Thus, IMUs can measure sagittal plane knee angles and ROM with high accuracy for a variety of tasks and may be a useful in-clinic tool for objective assessment of knee function following total knee arthroplasty.

Assessment of Ground Contact Time in the Field: Evaluation of Validity and Reliability

Abstract Weber, JA, Hart, NH, Rantalainen, T, Connick, M, and Newton, RU. Assessment of ground contact time in the field: evaluation of validity and reliability. J Strength Cond Res 38(1): e34–e39, 2024—The capacity to measure the kinetic and kinematic components of running has been extensively investigated in laboratory settings. Many authors have produced work that is of high value to practitioners within sporting environments; however, the lack of field-based technology to assess features of running gait validly and reliably has prevented the application of these valuable works. This paper examines the validity and reliability of a practical field-based methodology for using commercial inertial measurement units (IMUs) to assess ground contact time (GCT). Validity was examined in the comparison of GCT measured from ground reaction force by a force plate and that determined by a lumbar mounted commercial IMU and analyzed using a commercially available system (SPEEDSIG). Reliability was assessed by a field-based examination of within and between-session variability in GCT measured using a commercially available system (SPEEDSIG). Significance was set at p ≤ 0.05. Results for validity (intraclass correlation [ICC] 0.83) and reliability (ICC 0.91) confirm that the described field-based methodology is qualified for use to determine GCT in a practical setting. The implications of this study are important as they offer sport practitioners (S&amp;C coaches, rehab specialists, and physios) a scalable method to assess GCT in the field to develop greater understanding of their athletes and improve performance, injury prevention, and rehabilitation interventions. Furthermore, these results provide the foundation for further work that could provide greater detail describing individual running gait in the field.

Three-Dimensional Human Pose Estimation from Sparse IMUs through Temporal Encoder and Regression Decoder

Three-dimensional (3D) pose estimation has been widely used in many three-dimensional human motion analysis applications, where inertia-based path estimation is gradually being adopted. Systems based on commercial inertial measurement units (IMUs) usually rely on dense and complex wearable sensors and time-consuming calibration, causing intrusions to the subject and hindering free body movement. The sparse IMUs-based method has drawn research attention recently. Existing sparse IMUs-based three-dimensional pose estimation methods use neural networks to obtain human poses from temporal feature information. However, these methods still suffer from issues, such as body shaking, body tilt, and movement ambiguity. This paper presents an approach to improve three-dimensional human pose estimation by fusing temporal and spatial features. Based on a multistage encoder-decoder network, a temporal convolutional encoder and human kinematics regression decoder were designed. The final three-dimensional pose was predicted from the temporal feature information and human kinematic feature information. Extensive experiments were conducted on two benchmark datasets for three-dimensional human pose estimation. Compared to state-of-the-art methods, the mean per joint position error was decreased by 13.6% and 19.4% on the total capture and DIP-IMU datasets, respectively. The quantitative comparison demonstrates that the proposed temporal information and human kinematic topology can improve pose accuracy.

Introducing a time-efficient workflow for processing IMU data to identify sport-specific movement patterns

Introduction&#x0D; The use of inertial measurement unit (IMU) has become popular in sports assessment. New IMU devices may make the monitoring process easier; however, their validity and reliability should be established prior to widespread use. IMU devices use a combination of gyroscopic and accelerometer data which allow the derivation of velocity and position vectors by integrating the data over time. Because the process of time integration suffers from time varying biases and noise, the resulting velocity and position vectors are prone to drift after a few seconds. This must be accounted for when processing data from IMUs.&#x0D; Aim&#x0D; Motivated by the variety of approaches to IMU-based human motion tracking, the aim of this paper is to deliver a report of the author’s experience in processing and handling acceleration data from a wearable IMU sensor recorded during resistance training and present a workflow to identify specific movement patterns across different sports.&#x0D; Methods&#x0D; Given acceleration data from a wearable sensor during sports practice, the workflow to derive velocity and position measures of specific movement patterns is divided into the following seven steps: 1) Rough cropping of region of interest (ROI). 2) Application of low pass filter to remove jittering upon visual inspection. Depending on ROI length, a detrend filter should be applied on the integrated position and maybe on the velocity data to correct for drift. 3) Visual analysis of characteristics of at least one movement pattern (more if the pattern shows a high inter-repetition variability) to identify key events (e.g. maximal velocity). 4) Automatically find and count key events along ROI. 5) Reassess characteristics of movement pattern to determine other relevant events. 6) Next, segmentation of ROI based on selected events and integration of individual sections to avoid drift. The aim is to integrate the smallest pieces possible. 7) Finally, check to make sure that segmentation worked correctly (e.g. correct number of repetitions, resulting values in a possible range).&#x0D; Results&#x0D; Acceleration data was captured with an Apple Watch 7 (Apple Inc. California) using the SensorLog app streaming to a customized node.js server application. For the processing and visualization of the data, the programming language Python with usage of the Pandas and SciPy libraries were utilized. The velocity and position data were determined by finding the integral of the acceleration and velocity respectively. Using the previous mentioned workflow 306 repetitions of the back squat executed by 11 recreational athletes (w: 5/m: 6, age: 22-37, weight:58-90kg) were successful segmented.&#x0D; Discussion&#x0D; The technology underlying commercial IMU sensors are often not communicated transparently. Thus, it is important to properly study the task of calibration the IMU and calculating the vertical component before using it for sport science measurements. Furthermore, it is rarely the case that the movement pattern remains the same over each training session. Therefore, characteristics of the movement pattern must be studied thoroughly to create a robust identification criterium.&#x0D; By applying the presented workflow researcher have a structured, easy to apply and time efficient approach to analyze recorded acceleration data on different sport-specific movement patterns.

Bias-Repeatability Analysis of Vacuum-Packaged 3-Axis MEMS Gyroscope Using Oven-Controlled System.

The performance of microelectromechanical system (MEMS) inertial measurement units (IMUs) is susceptible to many environmental factors. Among different factors, temperature is one of the most challenging issues. This report reveals the bias stability analysis of an ovenized MEMS gyroscope. A micro-heater and a control system exploiting PID/PWM were used to compensate for the bias stability variations of a commercial MEMS IMU from BOSCH "BMI 088". A micro-heater made of gold (Au) thin film is integrated with the commercial MEMS IMU chip. A custom-designed micro-machined glass platform thermally isolates the MEMS IMU from the ambient environment and is vacuum sealed in the leadless chip carrier (LCC) package. The BMI 088 built-in temperature sensor is used for temperature sensing of the device and the locally integrated heater. The experimental results reveal that the bias repeatability of the devices has been improved significantly to achieve the target specifications, making the commercial devices suitable for navigation. Furthermore, the effect of vacuum-packaged and non-vacuum-packaged devices was compared. It was found that the bias repeatability of vacuum-packaged devices was improved by more than 60%.

Low-Cost Real-Time PPP GNSS Aided INS for CAV Applications

Many connected and autonomous vehicle (CAV) applications benefit from navigation technologies that reliably achieve lane-level accuracy. Global organizations have recently begun to provide real-time common-mode error corrections for Global Navigation Satellite Systems (GNSS) that enable this level of positioning accuracy using Precise Point Positioning (PPP). Incorporating an Inertial Measurement Unit (IMU) with a GNSS receiver can achieve this positioning performance reliably on moving platforms while providing a full state estimate, with high bandwidth at a high sampling rate. For commercial automotive applications cost is critical; therefore, this article focuses on commercial grade IMU’s and single-frequency GNSS code and Doppler measurements. This article presents and demonstrates a tightly-coupled PPP GNSS aided Inertial Navigation System (INS) using only publicly available real-time data sources. The article discusses the role of the INS in automotive applications and how the quality of the IMU affects the ability to achieve that role. The experimental results demonstrate positioning accuracy that surpasses the Society of Automotive Engineering (SAE) J2945 specification (horizontal error ≤1.5 m and vertical error ≤ 3 m 68%).

Variability of Jump Biomechanics Between Horses of Different Age and Experience Using Commercial Inertial Measurement Unit Technology

The application of commercial inertial measurement units has become popular in equestrian sports, which may help to eliminate a gap of knowledge concerning many aspects of biomechanics in training. This study employed the Seaver IMU system to measure jumping characteristics of horses with differing age-competition experience during regular training. It was hypothesized that experience level results in lower variability of jumping parameters. Twelve Warmblood horses aged 5 to 6 years with/without competition experience and 7 to 11 years with experience were investigated during regular training in 2 training centers. Consistent number of 10 successive jumps of the individual chosen course of vertical and spread obstacles (5th –15th) were analyzed and the following parameters were measured: jump height, reserve and length; taking off angle, acceleration, velocity; jump spatial shifting, energy by landing, and frequency of approach strides. Preliminary analysis confirmed comparable physiological effort in 2 training center based on heart rate, distance and duration measurements. The multifactorial analysis of variance for biomechanical data included in the statistical model the random effect of horse and fixed effects of training center, age-experience group, successive jump number, obstacle type and height. Four parameters were significantly different between the younger, inexperienced group and experienced younger and older horses: height of jump (P = .01), frequency of approach strides (P = .005), acceleration of taking off (P = .01), and energy by landing (P = .0013). Standard errors for almost all the parameters reached higher values for the youngest, inexperienced horses. Variability of jumping parameters was lower for experienced groups of horses, suggesting higher precision on obstacle courses.

A Study on the Dexterity of Surgical Robotic Tools in a Highly Immersive Virtual Environment: Assessing Usability and Efficacy

Robot-assisted minimally invasive surgery (RAMIS) has produced noticeable benefits for patients in the recent years <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , making it a favorable approach for a wide range of surgeries. The benefits of improving the dexterity of patient side manipulators to enable surgeons to perform more complex tasks are offset by the increased complexity of teleoperation and cognitive and physical effort on the operator side typically. A right balance between higher dexterity and intuitive control in teleoperation is yet to be defined. In this study, a dexterous, anthropomorphic primary master controller was deployed to assess and compare the efficiency of simulated anthropomorphic surgical instruments in an immersive surgical concept. Virtual surgical training tasks were built using a gaming software engine (Unity) and performed using simulated surgical tools with extended degrees of freedom (DoF) in the surgical shaft and gripper and compared with the standard da Vinci (DV) grasper. The motion of the tools were controlled using commercial inertial measurement unit (IMU) sensor-based devices attached to the user’s arms and hands. This article summarizes results obtained from three studies with similar features but different levels of complexity, taken with both lay users with no experience in surgery or teleoperation and surgeons experienced in RAMIS. The results showed that more than 70% of users achieved better results using articulated tools but required more physical and mental effort for teleoperation.

Stress Testing for Performance Analysis of Orientation Estimation Algorithms

Technological advancements have seen the spread of MEMS (Micro-electromechanical Systems) based sensors such as IMU (Inertial Measurement Unit) systems. The latter provide object orientation information in numerous applications such as smartphones, robotics, automotive, drones, and many others. In more detail, IMUs based on MEMS technology are characterized by small size, low power, and low cost. Technically, orientation information is provided by appropriate orientation estimation algorithms fed with quantities measured by MEMS sensors, such as acceleration, angular velocity, and magnetic field. A preliminary work pointed out that such MEMS sensors suffer significant drifts when they have to endure non-standard operating profiles in terms of thermal conditions. However, when considering very low-cost devices, the influence of temperature on the operation of orientation estimation algorithms still remains an important research gap in the literature. To deal with this important aspect, the paper proposes a specific test plan to analyze the performances of MEMS-based IMUs by considering a temperature stress test. Therefore, the effect of temperature was first analyzed on the individual sensors of the IMU (accelerometer, gyroscope, and magnetometer), then the effects of a very simple temperature compensation strategy were analyzed to highlight the importance of such step. Specifically, the effects of not compensating for temperature were studied considering a low-cost commercial IMU typically used in various fields of application. To better quantify such effects in practical applications, two well-known orientation estimation algorithms (i.e., Complementary Filter and AHRS) typically fed by IMUs output have been considered. The experimental results obtained using the custom devices under test show that temperature compensation is necessary for the best performance of the analyzed orientation estimation algorithms.

Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique.

Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.

Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

A Low-Complexity 6DOF Magnetic Tracking System Based on Pre-Computed Data Sets for Wearable Applications

We present a 6DOF magnetic tracking system based on a low-complexity algorithm, operating with an Inertial Measurement Unit (IMU) orientation estimation and regression functions formed with simulated data sets, capable of running using only a single microcontroller unit (MCU), for use in low-complexity wearable and wireless systems. A prototype based on a commercial magnetometer and IMU, a Cortex-M4 MCU was implemented and tested in both static and dynamic conditions, using a VICON motion tracking system as reference. Static and dynamic spatial accuracy performance is 2.6mm and 5.4mm respectively, after applying a calibration procedure based on a two layers Neural Network (NN) and a measured data set. Comparison with the state-of-the-art, supported by a defined Figure-of-Merit (FoM) show excellent performance compared to commercial and research systems in a low-complexity and portable solution.

Validity and Reliability of a New Device to Measure Type of Actions in Indoor Sports.

The purpose of this study was to analyze the validity and the reliability of the intensity ranges, number of actions and changes of direction measured by a commercial inertial measurement unit. Eleven elite youth futsal players performed a circuit with different type of displacements as sprinting, running at low-medium intensity, standing up and changes of direction. Data recorded by the Overtraq system were compared with video-analyzer during the six trials of each player. Standard error mean, Intraclass Correlation Coeficient and Coefficient of variation, were calculated to analyze the reliability of the device, as well as the Root Mean Square Error and Confidence Interval with correlation of Pearson for its validity. The results reported good validity for three intensity ranges (R2>0.7) with high reliability (Intraclass Correlation Coeficient: 0.8-0.9), especially for high intensity actions (Intraclass Correlation Coeficient: 0.95, Coefficient of Variation: 3.06%). Furthermore, the validity for the number of different actions was almost perfect (96.3-100%), with only small differences regarding changes of activity (mean error: 2.0%). The Overtraq system can be considered as a valid and reliable technology for measuring and monitoring actions at different intensities and changes of direction in futsal, likewise common actions for other indoor sports.

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications.

Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.

Human-in-the-Loop Assessment of an Ultralight, Low-Cost Body Posture Tracking Device.

In rehabilitation, assistive and space robotics, the capability to track the body posture of a user in real time is highly desirable. In more specific cases, such as teleoperated extra-vehicular activity, prosthetics and home service robotics, the ideal posture-tracking device must also be wearable, light and low-power, while still enforcing the best possible accuracy. Additionally, the device must be targeted at effective human-machine interaction. In this paper, we present and test such a device based upon commercial inertial measurement units: it weighs 575 g in total, lasts up to 10.5 h of continual operation, can be donned and doffed in under a minute and costs less than 290 EUR. We assess the attainable performance in terms of error in an online trajectory-tracking task in Virtual Reality using the device through an experiment involving 10 subjects, showing that an average user can attain a precision of 0.66 cm during a static precision task and 6.33 cm while tracking a moving trajectory, when tested in the full peri-personal space of a user.

Assessment of the measurement accuracy of inertial sensors during different tasks of daily living

The low cost and ease of use of inertial measurement units (IMUs) make them an attractive option for motion analysis tasks that cannot be easily measured in a laboratory. To date, only a limited amount of research has been conducted comparing commercial IMU systems to optoelectronic systems, the gold standard, for everyday tasks like stair climbing and inclined walking. In this paper, the 3D joint angles of the lower limbs are determined using both an IMU system and an optoelectronic system for twelve participants during stair ascent and descent, and inclined, declined and level walking. Three different datasets based on different hardware and anatomical models were collected for the same movement in an effort to determine the cause and quantify the errors involved with the analysis. Firstly, to calculate software errors, two different anatomical models were compared for one hardware system. Secondly, to calculate hardware errors, results were compared between two different measurement systems using the same anatomical model. Finally, the overall error between both systems with their native anatomical models was calculated. Statistical analysis was performed using statistical parametric mapping. When both systems were evaluated based on the same anatomical model, the number of trials with significant differences decreased markedly. Thus, the differences in joint angle measurement can mainly be attributed to the variability in the anatomical models used for calculations and not to the IMU hardware.

Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring.

Wearable sensors and systems have become increasingly popular in recent years. Two prominent wearable technologies for human activity monitoring are smart textile systems (STSs) and inertial measurement units (IMUs). Despite ongoing advances in both, the usability aspects of these devices require further investigation, especially to facilitate future use. In this study, 18 participants evaluate the preferred placement and usability of two STSs, along with a comparison to a commercial IMU system. These evaluations are completed after participants engaged in a range of activities (e.g., sitting, standing, walking, and running), during which they wear two representatives of smart textile systems: (1) a custom smart undershirt (SUS) and commercial smart socks; and (2) a commercial whole-body IMU system. We first analyze responses regarding the usability of the STS, and subsequently compared these results to those for the IMU system. Participants identify a short-sleeved shirt as their preferred activity monitor. In additional, the SUS in combination with the smart socks is rated superior to the IMU system in several aspects of usability. As reported herein, STSs show promise for future applications in human activity monitoring in terms of usability.

Gait evaluation using inertial measurement units in subjects with Parkinson’s disease

We investigated whether a wearable system based on a commercial Inertial Measurement Unit (IMU) can reliably provide the main spatiotemporal gait parameters in subjects with Parkinson’s disease (PD), compared to a gold-standard optoelectronic motion capture system.The gait of 22 subjects with PD (Age: 69.4 (6.1) years; UPDRS-III: 28.0 (9.2)) was recorded simultaneously with an optoelectronic system and a commercial IMU-based wearable system. Eight spatiotemporal parameters describing the step cycle (cadence, velocity, stride length, stride duration, step length, stance, swing and double support duration) were compared between the two systems. The IMU and the optical system reported comparable gait parameters, with the exception of walking velocity (optical system, 0.72 (0.27) m∙s−1 vs. IMU: 0.86 (0.26) m∙s−1, p < 0.05). Although most parameters detected by the two systems were not statistically different, some of them like stride length, double support and step duration showed notable root mean square and mean absolute errors. In conclusion, the algorithm embedded in the current release of the commercial IMU requires further improvements to be properly used with subjects with PD. Overall, the IMU system was sufficiently accurate in the assessment of fundamental gait spatiotemporal parameters. The fast and simplified data recording process allowed by wearables makes this technology appealing and represents a possible solution for the quantification of gait in the clinical context, especially when using a traditional 3D optoelectronic gait analysis is not possible, and when subjects are not fully cooperative.

Convergent Validity of a Wearable Sensor System for Measuring Sub-Task Performance during the Timed Up-and-Go Test

Background: The timed-up-and-go test (TUG) is one of the most commonly used tests of physical function in clinical practice and for research outcomes. Inertial sensors have been used to parse the TUG test into its composite phases (rising, walking, turning, etc.), but have not validated this approach against an optoelectronic gold-standard, and to our knowledge no studies have published the minimal detectable change of these measurements. Methods: Eleven adults performed the TUG three times each under normal and slow walking conditions, and 3 m and 5 m walking distances, in a 12-camera motion analysis laboratory. An inertial measurement unit (IMU) with tri-axial accelerometers and gyroscopes was worn on the upper-torso. Motion analysis marker data and IMU signals were analyzed separately to identify the six main TUG phases: sit-to-stand, 1st walk, 1st turn, 2nd walk, 2nd turn, and stand-to-sit, and the absolute agreement between two systems analyzed using intra-class correlation (ICC, model 2) analysis. The minimal detectable change (MDC) within subjects was also calculated for each TUG phase. Results: The overall difference between TUG sub-tasks determined using 3D motion capture data and the IMU sensor data was <0.5 s. For all TUG distances and speeds, the absolute agreement was high for total TUG time and walk times (ICC > 0.90), but less for chair activity (ICC range 0.5–0.9) and typically poor for the turn time (ICC < 0.4). MDC values for total TUG time ranged between 2–4 s or 12–22% of the TUG time measurement. MDC of the sub-task times were higher proportionally, being 20–60% of the sub-task duration. Conclusions: We conclude that a commercial IMU can be used for quantifying the TUG phases with accuracy sufficient for clinical applications; however, the MDC when using inertial sensors is not necessarily improved over less sophisticated measurement tools.