Use Of Inertial Sensors Research Articles

A switched-gain nonlinear observer for estimation of thoracoabdominal displacements and detection of asynchrony

This paper develops and evaluates a wearable sensor-based system for the estimation of three-dimensional thoracoabdominal displacements and the detection of respiratory asynchrony between chest and abdomen. Such estimation is useful for a number of respiratory diagnosis applications, including detection of paradoxical breathing, quantification of tidal volume, and decision-support for initiation of, or weaning from, mechanical ventilation. The use of inertial sensors on the body to estimate respiratory displacements is challenging due to the tilting of the body that occurs with breathing, causing continuous changes in the gravity component at the same frequency as the breathing frequency. A method to estimate the front-to-back and side-to-side tilt angles is developed using a nonlinear observer. Due to the complex multivariable nonlinear measurement functions involved, a switched gain nonlinear observer is found to be necessary. The global stability of the observer is analyzed using Lyapunov analysis. Respiratory asynchrony is evaluated using Lissajous curves and a cross correlation phase angle calculation method. Example experimental results are presented using data from three subjects at various breathing frequencies, tidal volumes, and inspiratory resistances. Displacement estimates are found to be typically within ± 1 mm compared to a gold standard reference for most data sets. Phase angles are found to increase monotonically with inspiratory resistive or elastic loading.

Integrating human-centric simulations in educational production lines: advancing ergonomics for industry 5.0 applications

This research in the Industry 5.0 field focuses on a human-centered simulation of the FAS200 SMC educational production line, utilizing Tecnomatix Process Simulate Human software for developing a virtual human environment. A key aspect of this study is the integration of inertial sensors, enhancing the accuracy and depth of ergonomic analysis. These sensors play a pivotal role in capturing precise human movement data, crucial for ergonomic assessments. Adopting a defined working methodology, the study extensively employs the RULA method to evaluate operator postures in the production line. This approach has led to significant ergonomic improvements, evidenced by a 40 percent reduction in the RULA index at each workstation. The integration of inertial sensors has been instrumental in achieving these results, providing detailed insights into human movements and interactions with the production environment. The research transcends traditional ergonomic assessments by incorporating a new human-centered approach, emphasizing the well-being of individuals working alongside machines. This approach, bolstered by the use of inertial sensors, marks a significant advancement in ergonomic studies, aligning with the principles of Industry 5.0. The findings hold substantial potential for application in industrial settings, signaling a shift towards more human-friendly and efficient industrial practices.

Exploring the Potential of Machine Learning Algorithms Associated with the Use of Inertial Sensors for Goat Kidding Detection.

The autonomous identification of animal births has a significant added value, since it enables for a prompt timely human intervention in the process, protecting the young and the mothers' health, without requiring continuous human surveillance. Wearable inertial sensors have been employed for a variety of animal monitoring applications, thanks to their low cost and the fact that they allow less invasive monitoring process. Alarms triggered by the occurrence of events must be generated close to the events to avoid delays caused by communication latency, which is why this type of mechanism is typically implemented at the network's edge and integrated with existing auxiliary mechanisms on the Internet. Although the detection of births in cattle has been carried out commercially for some years, there is no solution for small ruminants, especially goats, where the literature does not even report any attempts. The current work consisted of a first attempt at developing an automatic birth monitor using inertial sensing, as well as detection techniques based on Machine Learning, implemented in a network edge device to assure real-time alarm triggering. Thus, two concept drift detection techniques and seven kidding detection mechanisms were developed using data classification models. The work also includes the testing and comparison of learning results, both in terms of accuracy and of computational costs of the detection module, for algorithms implemented. The results revealed that, despite their simplicity, concept drift algorithms do not allow kidding detection, whereas classification-algorithm-based static learning models do, despite the unbalanced character of the dataset and its reduced size. The learning findings are quite promising in terms of computational cost and its suitability for deployment on edge devices. The algorithm demonstrates behavior changes four hours before kidding and allows for the identification of the kidding hour with an accuracy of 61%, as well as the capacity to improve the overall learning process with a larger dataset.

Online Ergonomic Evaluation in Realistic Manual Material Handling Task: Proof of Concept.

Work-related musculoskeletal disorders are globally one of the leading causes of work-related injuries. They significantly impact worker health and business costs. Work task ergonomic risk indices have been developed that use observational assessments to identify potential injuries, and allow safety managers to promptly intervene to mitigate the risks. However, these assessments are very subjective and difficult to perform in real time. This work provides a technique that can digitalize this process by developing an online algorithm to calculate the NIOSH index and provide additional data for ergonomic risk assessment. The method is based on the use of inertial sensors, which are easily found commercially and can be integrated into the industrial environment without any other sensing technology. This preliminary study demonstrates the effectiveness of the first version of the Online Lifting Index (On-LI) algorithm on a common industrial logistic task. The effectiveness is compared to the standard ergonomic assessment method. The results report an average error of 3.6% compared to the NIOSH parameters used to calculate the ergonomic risk and a relative error of the Lifting Index of 2.8% when compared to observational methods.

Gait Alteration in Individual with Limb Loss: The Role of Inertial Sensors.

Amputation has a big impact on the functioning of patients, with negative effects on locomotion and dexterity. In this context, inertial measurement units represent a useful tool in clinical practice for motion analysis, and in the development of personalized aids to improve a patient's function. To date, there is still a gap of knowledge in the scientific literature on the application of inertial sensors in amputee patients. Thus, the aim of this narrative review was to collect the current knowledge on this topic and stimulate the publication of further research. Pubmed, Embase, Scopus, and Cochrane Library publications were screened until November 2022 to identify eligible studies. Out of 444 results, we selected 26 articles focused on movement analysis, risk of falls, energy expenditure, and the development of sensor-integrated prostheses. The results showed that the use of inertial sensors has the potential to improve the quality of life of patients with prostheses, increasing patient safety through the detection of gait alteration; enhancing the socio-occupational reintegration through the development of highly technologic and personalized prosthesis; and by monitoring the patients during daily life to plan a tailored rehabilitation program.

Estimation of Respiratory Displacements Using a Nonlinear Observer

This paper develops and evaluates a wearable sensor-based system for the estimation of three-dimensional thoracoabdominal displacements. Such estimation is useful for a number of respiratory diagnosis applications, including detection of paradoxical breathing, quantification of tidal volume, and determining efficiency of mechanical ventilation. The use of inertial sensors on the body to estimate respiratory displacements is challenging due to the tilting of the body that occurs with breathing, causing continuous changes in the gravity component at the same frequency as the breathing frequency. A method to estimate the front-to-back and side-to-side tilt angles is developed using a nonlinear observer. The global stability of the observer is analyzed using Lyapunov analysis. Example experimental results are presented using data from a supine subject at various breathing frequencies and tidal volumes. Displacement estimates are found to be typically within ±1 mm compared to a gold standard reference for most data sets.

Adaptive Data Transmission Algorithm for the System of Inertial Sensors for Hand Movement Acquisition.

Modern systems of intelligent sensors commonly use radio data transmission. Hand movement acquisition with the use of inertial sensors requires the processing and transmission of a relatively large amount of data, which may be associated with a significant load on the network structure. Network traffic limitation, without losing the quality of monitoring parameters from the sensor system, is therefore important for the functioning of the radio network which integrates both the teletransmission sensor system and the data acquisition server. The paper presents a wearable solution for hand movement acquisition, which uses data transmission in the Wi-Fi standard and contains 16 MEMS (Micro Electro Mechanical System) sensors. An adaptive algorithm to control radio data transmission for the sensor system has been proposed. The algorithm implemented in the embedded system controls the change of the frame length, the length of the transmission frame and the frequency of its sending, which reduces the load on the network router. The use of the algorithm makes it possible to reduce the power consumption by the sensor system by up to 19.9% and to limit the number of data transferred by up to about 91.6%, without losing the quality of the monitored signal. The data analysis showed no statistically significant differences (p > 0.05) between the signal reconstructed from the complete data and processed by the algorithm.

Application of an Inertial Sensor Unit for Position Estimation and Motion Control of the Lower-Extremity Powered Exoskeleton

Introduction. The problem of controlling the lower-extremity powered exoskeleton motion was investigated. To solve it, it was proposed to use a program control and feedback control. The formation of control in the form of feedback required an assessment of the state of the exoskeleton (rotation angles, angular velocities, and accelerations of the links). The possibility of using an inertial measuring unit to estimate angular velocities and accelerations of exoskeleton links was considered. The work objective was to develop laws for the formation of the exoskeleton motion control, which could provide the stability of the program motion and use the measurements of encoders, micromechanical gyroscopes and accelerometers.Materials and Methods. Previously performed mathematical modeling of the exoskeleton dynamics was used to form a program control. It was proposed to equip the exoskeleton with inertial sensor units. This solution made it possible to evaluate the state vector of the exoskeleton and to use these estimates in a feedback loop. A mathematical model of measurements of these sensors was described. The proposed version is suitable for control systems of three-link exoskeletons of the lower extremities and can be expanded to the case of multi-link exoskeleton designs.Results. New laws of exoskeleton motion control based on a mathematical model of the system dynamics and using measurement information from encoders and inertial information sensors were proposed. Numerical simulation of exoskeleton motion was performed in the Wolfram Mathematica mathematical package. Its results confirmed the operability of the proposed control and the possibility of using an inertial sensor unit to assess the exoskeleton state. The numerical simulation results for the following program movements were presented: lifting the exoskeleton from a sitting position to a vertical position, and stabilization of the vertical equilibrium position.Discussion and Conclusions. The proposed control can be applied in exoskeletons for medical purposes, e.g., in the task of verticalization of patients with dysfunctions of the musculoskeletal system. The possibility of using measurement information obtained from inertial measurements units in the problem of estimating the state of exoskeleton links was demonstrated. The use of inertial sensors will make it possible to determine the angular acceleration of the exoskeleton links, avoiding numerical differentiation of the measurement information received from the encoders. The obtained estimates of angular acceleration allow us to introduce feedback on angular accelerations into the control system, which opens up the possibility of improving transients in controlling the exoskeleton motion.

Fabricated Components Hoisting Activity Recognition and Collision Analysis Based on Inertial Measurement Unit IMU

The prefabricated components are an important aspect of prefabricated constructions. Based on three possible collision situations when the fabricated component is being lifted during the installation phase, the aim of this study is to investigate how to prevent effective collision between the hoist and the barrier, and establish the standardized operation process (SOP) of using the Inertial Measurement Unit (IMU) to detect the hoisting collision of fabricated building components. The (IMU) technology is used in the collection of lifting activities data. The hoisting activity will be divided into four situations: classification and recognition of stationary, ascending, advancing, and descending of components; use of the K-Nearest Neighbor (KNN) algorithm and the Random Forest (RF) algorithm for data processing to recognize the hoisting activities of assembled components; construction of the hoisting activity recognition model; determination of the best recognition position of the IMU; further collision analysis based on the recognition of the hoisting of assembled components. The collision is divided into direct collision, sudden stop, and detour in a specific space from obstacles. Image analysis of the three types of collision activities will be carried out to help perceive lifting activities in advance and reduce loss resulting from collisions caused by components. According to the systematic research and discussion of hoisting activity recognition and collision behavior, it provides a reasonable basis and ideas for solving hoisting collisions in prefabricated buildings and aids in the use of inertial sensors in construction to provide assistance for construction automation.

Inertial Measurement Units and Application for Remote Health Care in Hip and Knee Osteoarthritis: Narrative Review.

BackgroundMeasuring and modifying movement-related joint loading is integral to the management of lower extremity osteoarthritis (OA). Although traditional approaches rely on measurements made within the laboratory or clinical environments, inertial sensors provide an opportunity to quantify these outcomes in patients’ natural environments, providing greater ecological validity and opportunities to develop large data sets of movement data for the development of OA interventions.ObjectiveThis narrative review aimed to discuss and summarize recent developments in the use of inertial sensors for assessing movement during daily activities in individuals with hip and knee OA and to identify how this may translate to improved remote health care for this population.MethodsA literature search was performed in November 2018 and repeated in July 2019 and March 2021 using the PubMed and Embase databases for publications on inertial sensors in hip and knee OA published in English within the previous 5 years. The search terms encompassed both OA and wearable sensors. Duplicate studies, systematic reviews, conference abstracts, and study protocols were also excluded. One reviewer screened the search result titles by removing irrelevant studies, and 2 reviewers screened study abstracts to identify studies using inertial sensors as the main sensing technology and a primary outcome related to movement quality. In addition, after the March 2021 search, 2 reviewers rescreened all previously included studies to confirm their relevance to this review.ResultsFrom the search process, 43 studies were determined to be relevant and subsequently included in this review. Inertial sensors have been successfully implemented for assessing the presence and severity of OA (n=11), assessing disease progression risk and providing feedback for gait retraining (n=7), and remotely monitoring intervention outcomes and identifying potential responders and nonresponders to interventions (n=14). In addition, studies have validated the use of inertial sensors for these applications (n=8) and analyzed the optimal sensor placement combinations and data input analysis for measuring different metrics of interest (n=3). These studies show promise for remote health care monitoring and intervention delivery in hip and knee OA, but many studies have focused on walking rather than a range of activities of daily living and have been performed in small samples (<100 participants) and in a laboratory rather than in a real-world environment.ConclusionsInertial sensors show promise for remote monitoring, risk assessment, and intervention delivery in individuals with hip and knee OA. Future opportunities remain to validate these sensors in real-world settings across a range of activities of daily living and to optimize sensor placement and data analysis approaches.

Use of Wearable Sensors to Assess the Effects of Performing a Cognitive Task on Sensory Integration of Balance in Healthy Individuals.

This study investigated the effects of performing a cognitive task on the sensory integration of balance in healthy individuals. Ten subjects (five F/five M; 21.5 ± 2.17 years; 69.9 ± 3.4 inches; 155.6 ± 26.1 lbs; Caucasian), without known balance issues, performed the modified Clinical Test of Sensory Interaction of Balance (mCTSIB) with and without a cognitive task. The cognitive task involved counting down in threes from a randomly assigned number between 95 and 100. Postural sway area and postural sway jerk were assessed through the use of inertial sensors placed around the subjects’ lower lumbar region. Each subject performed four trials for the four conditions of the mCTSIB: eyes open firm (EOFirm), eyes closed firm (ECFirm), eyes open foam (EOFoam), and eyes closed foam (ECFoam). We tested the effect of performing a cognitive task on the sensory integration of balance. We hypothesized that sensory cognitive interaction would be more apparent for more complex conditions and would be better assessed with postural sway jerk compared to postural sway area measure. With the addition of a cognitive task for the mCTSIB: (1) postural sway area increased in the baseline condition, i.e., EOFirm (p < 0.05), but did not increase in the most difficult condition, i.e., ECFoam; (2) postural sway jerk increased in all conditions of the mCTSIB (p < 0.05); (3) cognitive performance did not deteriorate across conditions of the mCTSIB. Postural sway jerk was shown to be a more sensitive measure in detecting the effect of a cognitive task on sensory integration for postural control. Overall, inertial sensors can be used to reliably assess postural sway differences related to sensory–cognitive integration.

Pose estimation and mapping based on IMU and LiDAR

In the field of autonomous motion and mapping, estimating the position and orientiation of a moving body is of high significance. This includes in particular the latest information about acceleration, speed, traveled distance and orientation. A wide range of different technologies exists that can be applied for this purpose. In case the state determination requires the use of inertial sensors only, it is well known that these types of sensors tend to drift with time. This leads to increasing errors in the calculated trajectory, which is unacceptable if precise position information is required such as for mapping. In this paper we present a method which significantly enhances the position estimation of inertial sensors by using a 2D light detection and ranging (LiDAR) sensor.

Classification of Muscle Inertial Motion and Electromyographic Activity Integration to Improve Accuracy in Pattern Recognition

ABSTRACT Introduction Over the years, several studies have been published reporting the use of distinct sources of information used for pattern recognition that can be translated into commands to control human-machine interface system, for example, electromyography (EMG), pressure sensors, and accelerometers. Studies using muscle motion patterns and its combination with EMG in the context of pattern recognition for evaluation of the muscles and human-machine interface system in able-bodied individuals and limb-absent subjects are scarce. Material and Methods In this context, this research presents the assessment of the classification of patterns formed by features extracted from both muscle motion and electromyographic signals. Data sets were collected from both arms of five unilateral transradial limb-absent subjects and seven able-bodied subjects in the control group. The features from the EMG and the muscle motion such as amplitude, frequency, predictability, and variability of the signals were estimated. Results The results were presented in terms of the sensitivity, specificity, precision, and accuracy of the classifier. The combination of both measurements, EMG and muscle motion, defined the six basic movements for limb-absent subjects within an accuracy of 98% ± 1% for the sound forearm against 96% ± 4% for the amputated forearm. Conclusions For future work, it is expected that the strategy of classification and the combination of inertial and electromyographic activity will be used in actual scenarios for the controlling of artificial limbs and other applications related to human-machine interaction. Clinical Relevance The use of inertial sensors may increase the usability and accuracy of systems used for diagnosing, training, therapy, or controlling devices such as orthoses and prostheses.

A Comparative Evaluation of Inertial Sensors for Gait and Jump Analysis.

Gait and jump anomalies are often used as indicators to identify the presence and state of disorders that involve motor symptoms. Physical tests are often performed in specialized laboratories, which offer reliable and accurate results, but require long and costly analyses performed by specialized personnel. The use of inertial sensors for gait and jump evaluation offers an easy-to-use low-cost alternative, potentially applicable by the patients themselves at home. In this paper, we compared three inertial measurement units that are available on the market by means of well-known standardized tests for the evaluation of gait and jump behavior. The aim of the study was to highlight the strengths and weaknesses of each of the tested sensors, considered in different tests, by comparing data collected on two healthy subjects. Data were processed to identify the phases of the movement and the possible inaccuracies of each sensor. The analysis showed that some of the considered inertial units could be reliably used to identify the gait and jump phases and could be employed to detect anomalies, potentially suggesting the presence of disorders.

Validity and reliability of innovative field measurements of tibial accelerations and spinal kinematics during cricket fast bowling

The use of inertial sensors in fast bowling analysis may offer a cheaper and portable alternative to current methodologies. However, no previous studies have assessed the validity and reliability of such methods. Therefore, this study aimed to assess the validity and reliability of collecting tibial accelerations and spinal kinematics using inertial sensors during in vivo fast bowling. Thirty-five elite male fast bowlers volunteered for this study. An accelerometer attached to the skin over the tibia was used to determine impacts and inertial sensors over the S1, L1 and T1 spinous processes used to derive the relative kinematics. These measurements were compared to optoelectronic and force plate data for validity analysis. Most acceleration and kinematics variables measured report significant correlations > 0.8 with the corresponding gold standard measurement, with intraclass correlation coefficients greater than 0.7. Low standard error of measurement and consequently small minimum detectable change (MDC) values were also observed. This study demonstrates that inertial sensors are as valid and reliable as current methods of fast bowling analysis and may provide some advantages over traditional methods. The novel metrics and methods described in this study may aid coaches and practitioners in the design and monitoring of fast bowling technique.Graphical abstractGraphical abstract illustrating the synopsis of the findings from this paper.

Reliability of Human Running Analysis With Low-Cost Inertial and Magnetic Sensor Arrays

In the last years, the use of inertial sensors has increased due to their time efficiency and portability. This paper introduces a low-cost inertial system for gait and running analysis embedded on running tights, S-Move, designed with an open-source orientation estimation algorithm. Calibration and misalignment correction procedure are presented, as well as methods and algorithms that automatically assess mean stride kinematics and spatiotemporal parameters. The main objective of this study was to validate S-Move reliability through comparison of its repeatability with a commonly used optoelectronic device for angular kinematic stride and an instrumented treadmill for spatiotemporal parameters. Thirty subjects performed three sessions of walking and running being alternatively equipped by two operators. To analyze repeatability of devices, multivariate ANOVAs were performed on the measured differences between sessions. Direct comparisons between devices were also conducted. Devices comparison showed that both devices presented a moderate absolute reproducibility – between 2 and 10° – and a very good relative (after mean suppression) reproducibility – max. 4.5°. A non-negligible measure difference between systems was observed but it could not be attributed to the lack of accuracy of one device. Regarding spatiotemporal parameters, treadmill measurement was very reproducible – about 2%. S-Move reproducibility and accuracy was similar on temporal parameters but lower on spatial parameters – about 5%. However, accuracy was concordant with other studies using inertial devices. Finally, this study showed that accessible, embeddable and automatic gait and running analysis is suitable for sports performance analysis and rehabilitation is possible, at very low costs.

Estimation of Lateral Track Irregularity Through Kalman Filtering Techniques

The aim of this work is to develop a model-based methodology for monitoring lateral track irregularities based on the use of inertial sensors mounted on an in-service train. To this end, a gyroscope is used to measure the wheelset yaw angular velocity and two accelerometers are used to measure lateral acceleration of the wheelset and the bogie frame. The main contribution of the present work is the development of a very efficient Kalman-based monitoring strategy to estimate the lateral track irregularities. The Kalman filter is based on a highly simplified linear bogie model that is able to capture the most relevant dynamic behaviour of the vehicle. The behaviour of the designed filter is assessed through the use of a detailed multibody model of an in-service vehicle running on a straight track with realistic irregularities. The model output is used to generate virtual measurements that are subsequently used to run the filter and validate the proposed estimator. In addition, the equivalent parameters of the simplified model are identified based on these simulations. In order to prove the robustness of the proposed technique, a systematic parametric analysis has been performed. The results obtained with the proposed method are promising, showing high accuracy and robustness for monitoring lateral alignment on straight tracks, with a very low computational cost.

A novel use of inertial sensors to measure the craniocervical flexion range of motion associated to the craniocervical flexion test: an observational study

BackgroundThe craniocervical flexion test (CCFT) is recommended when examining patients with neck pain related conditions and as a deep cervical retraining exercise option. During the execution of the CCFT the examiner should visually assess that the amount of craniocervical flexion range of motion (ROM) progressively increases. However, this task is very subjective. The use of inertial wearable sensors may be a user-friendly option to measure and objectively monitor the ROM. The objectives of our study were (1) to measure craniocervical flexion range of motion (ROM) associated with each stage of the CCFT using a wearable inertial sensor and to determine the reliability of the measurements and (2) to determine craniocervical flexion ROM targets associated with each stage of the CCFT to standardize their use for assessment and training of the deep cervical flexor (DCF) muscles.MethodsAdults from a university community able to successfully perform the CCFT participated in this study. Two independent examiners evaluated the CCFT in two separate sessions. During the CCFT, a small wireless inertial sensor was adhered to the centre of the forehead to provide real-time monitoring and to record craniocervical flexion ROM. The intra- and inter-rater reliability of the assessment of craniocervical ROM was calculated. This study was approved by the Research Ethics Committee of CEU San Pablo University (236/17/08).ResultsFifty-six participants (18 males, 23 females; mean [SD] age, 21.8 [3.45] years) were included in the study and successfully completed the study protocol. All interclass correlation coefficient (ICC) values indicated good or excellent reliability of the assessment of craniocervical ROM using a wearable inertial sensor. There was high variability between subjects on the amount of craniocervical ROM necessary to achieve each stage of the CCFT.ConclusionsThe use of inertial sensors is a reliable method to measure the craniocervical flexion ROM associated with the CCFT. The great variability in the ROM limits the possibility to standardize a set of targets of craniocervical flexion ROM equivalent to each of the pressure targets of the pressure biofeedback unit.

Motion Reveal Emotions: Identifying Emotions From Human Walk Using Chest Mounted Smartphone

Emotion recognition via gait analysis is an active and key area of research because of its significant academic and commercial potential. With recent developments in hardware technology, the use of inertial sensors allows researchers to effectively capture the human motion data for gait analysis. To this end, the aim of this paper is to identify emotions from the inertial signals of human gait recorded by means of body mounted smartphones. We extracted a manually-crafted set of features computed from the human inertial gait data which are used to train and precisely predict the human emotions. Specifically, we collected the inertial gait data of 40 volunteers by means of smartphone’s on-board inertial measurement units (3D accelerometer, 3D gyroscope) attached at the chest in six basic emotions including sad, happy, anger, surprise, disgust and fear . Using stride based segmentation, the raw signals are first decomposed into individual strides. For each stride, a set of 296 spectro-temporal features are computed, which are fed into two supervised learning predictors namely Support Vector Machines and Random Forest. The classification results obtained with the proposed methodology and validated with k-fold validation procedure show classification accuracy of 95% for binary emotions and 86% for all six categories of emotions.

The knee prosthesis constraint dilemma: Biomechanical comparison between varus‐valgus constrained implants and rotating hinge prosthesis. A cadaver study

The real degree of constriction of rotating hinge knee (RHK) and condylar constrained prostheses (CCK) is a matter of discussion in revision knee arthroplasty. The objectives of this study are to compare the tibial rotation of both implants and validate the use of inertial sensors with optical tracking system as movement measurement tools. A total of 16 cadaver knees were used. Eight knees were replaced using a RHK (Endomodel LINK), and the remaining eight received a CCK prosthesis (LCCK, Zimmer). Tibial rotation range of motion was measured in full extension and at 30°, 60°, and 90° of flexion, with four continuous waveforms for each measurement. Measurements were made using two inertial sensors with specific software and compared with measurements obtained using the gold standard technique - the motion capture camera. The comparison of the accuracy of both measurement methods showed no statistically significant differences between inertial sensors and motion capture cameras, with p > .1; the mean error for tibial rotation was 0.21°. Tibial rotation in the RHK was significantly greater than in the CCK (5.25° vs. 2.28°, respectively), p < .05. We have shown that RHK permit greater tibial rotation, being closer to physiological values than CCKs. Inertial sensors have been validated as an effective and accurate method of measuring knee movement. The clinical significance: RHK appears to represent a lower constriction degree than CCK systems.