Wearable Inertial Measurement Units Research Articles

Kinect and wearable inertial sensors for motor rehabilitation programs at home: state of the art and an experimental comparison

BackgroundEmerging sensing and communication technologies are contributing to the development of many motor rehabilitation programs outside the standard healthcare facilities. Nowadays, motor rehabilitation exercises can be easily performed and monitored even at home by a variety of motion-tracking systems. These are cheap, reliable, easy-to-use, and allow also remote configuration and control of the rehabilitation programs. The two most promising technologies for home-based motor rehabilitation programs are inertial wearable sensors and video-based motion capture systems.MethodsIn this paper, after a thorough review of the relevant literature, an original experimental analysis is reported for two corresponding commercially available solutions, a wearable inertial measurement unit and the Kinect, respectively. For the former, a number of different algorithms for rigid body pose estimation from sensor data were also tested. Both systems were compared with the measurements obtained with state-of-the-art marker-based stereophotogrammetric motion analysis, taken as a gold-standard, and also evaluated outside the lab in a home environment.ResultsThe results in the laboratory setting showed similarly good performance for the elementary large motion exercises, with both systems having errors in the 3–8 degree range. Usability and other possible limitations were also assessed during utilization at home, which revealed additional advantages and drawbacks for the two systems.ConclusionsThe two evaluated systems use different technology and algorithms, but have similar performance in terms of human motion tracking. Therefore, both can be adopted for monitoring home-based rehabilitation programs, taking adequate precautions however for operation, user instructions and interpretation of the results.

COMPARISON OF THREE DIFFERENT MEASUREMENT SYSTEMS TO ASSESS THE VERTICAL JUMP HEIGHT

ABSTRACT Introduction: The numerous instruments used to measure jump height use different technologies and calculations that can provide variable results. Objective: This study compared the countermovement jump (CMJ) height assessed with a wearable 3D inertial measurement unit (IMU), using flight time and the numerical integration method with a force platform and photocells. Methods: Forty CMJs were analysed, starting from an upright standing position with the hands placed on the waist. Twenty healthy volunteers completed 2 CMJs, which were simultaneously assessed using an IMU placed on the subject's sacrum, a force platform (considered the gold standard method) and photocells. The maximum height of each CMJ was measured. Results: The results showed a significant overestimation (p<0.001) in jump height for the IMU using the numerical integration method when compared to the force platform (+7 cm). Excellent intraclass correlation coefficients (ICCs) were obtained with the flight time equations for the different types of measurement equipment. Fair to good ICCs were obtained with the IMU using the numerical integration method and force platform. Conclusion: In conclusion, the jump height obtained with the IMU using the numerical integration method showed the poorest agreement compared to the force platform. Level of evidence III; Prospective comparative study.

Does kinematic gait quality improve with functional gait recovery? A longitudinal pilot study on early post-stroke individuals

Typical clinical gait outcomes mostly focus on function; only sparse information exists on gait quality, i.e. symmetry or more natural gait patterns. It remains unclear whether functional gait recovery improves with gait quality, or whether these are two independent processes. The objective of this observational pilot study is to examine whether the gait quality improves with gait function (i.e. speed) over the course of early recovery. Full lower body gait kinematics were measured longitudinally in a clinical environment using wearable inertial measurement units. We recorded six individuals with subacute stroke (<1 month) for a total of 56 physical therapy sessions over the initial recovery stage of 12 weeks. We examined relations between gait symmetry in spatiotemporal, limb and joint kinematic parameters compared to gait function. We observed that overall gait symmetry improved with walking speed, but limb and joint kinematic parameters remained asymmetric at the maximum level of recovery (both p < 0.01). We also found that limb kinematic parameters (R2 = 41.9%) of the impaired side was preferentially associated with functional gait recovery over joint kinematics (R2 = 33.1%). These data suggest that our pilot cohort did not achieve “true” gait recovery despite achieving typical measures of recovery in gait speed and spatiotemporal symmetry. These initial results illustrate the multifaceted nature of recovery and justify further research on monitoring gait quality with a larger clinical study, providing insight for more effective training regimens.

Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement.

Participation in sports has risen in the United States over the last few years, increasing the risk of injuries such as tears to the anterior cruciate ligament (ACL) in the knee. Previous studies have shown a correlation between knee kinematics when landing from a jump and this injury. The purpose of this study was to validate the ability of a commercially available inertial measurement units (IMUs) to accurately measure knee joint angles during a dynamic movement. Eight healthy subjects participated in the study. Validation was performed by comparing the angles measured by the wearable device to those obtained through the gold standard motion capture system when landing from a jump. Root mean square, linear regression analysis, and Bland–Altman plots were performed/constructed. The mean difference between the wearable device and the motion capture data was 8.4° (flexion/extension), 4.9° (ab/adduction), and 3.9° (rotation). In addition, the device was more accurate at smaller knee angles. In our study, a commercially available wearable IMU was able to perform fairly well under certain conditions and was less accurate in other conditions.

Gait recognition via random forests based on wearable inertial measurement unit

In recent years, gait detection has been widely used in medical rehabilitation, smart phone, criminal investigation, navigation and positioning and other fields. With the rapid development of micro-electro mechanical systems, inertial measurement unit (IMU) has been widely used in the field of gait recognition with many advantages, such as low cost, small size, and light weight. Therefore, this paper proposes a gait recognition algorithm based on IMU, which is named as FPRF-GR. Firstly, a fusion feature engineering operator is designed to eliminate redundant and defective features, which is mainly based on Fast Fourier Transform and principal component analysis. Then, in the design of classifier, in order to meet the requirements of gait recognition model for accuracy, generalization ability, speed, and noise resistance, this paper compares random forest (RF) and several commonly used classification algorithms, and finds that the model constructed by RF can meet the requirements. FPRF-GR builds the model based on RF, and uses the tenfold cross validation method to evaluate the model. Finally, this paper proposes an optimization scheme for the two parameters of decision tree number and sample number in RF. The results show that FPRF-GR can identify five gaits (walk, stationary, run, and up and down stairs) with the average accuracy of 98.2%.

Measuring Ergonomic Risk in Operating Surgeons by Using Wearable Technology

This case series study examines the ergonomic risk of surgery using wearable sensor inertial measurement units to monitor the ergonomics of surgeons at work.

Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments.

BackgroundWalking patterns can provide important indications of a person’s health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differently in lab versus real-world environments and whether age dependencies exist.MethodsThe walking patterns of 20 young and 20 elderly healthy subjects were recorded with five wearable inertial measurement units (ZurichMOVE sensors) attached to both ankles, both wrists and the chest. An automated detection process based on dynamic time warping was developed to efficiently identify the relevant sequences. From the ZurichMOVE recordings, 15 spatio-temporal gait parameters were extracted, analyzed and compared between motion patterns captured in a controlled lab environment (10 m walking test) and the non-controlled ecologically valid real-world environment (72 h recording) in both groups.ResultsSeveral parameters (Cluster A) showed significant differences between the two environments for both groups, including an increased outward foot rotation, step width, number of steps per 180° turn, stance to swing ratio, and cycle time deviation in the real-world. A number of parameters (Cluster B) showed only significant differences between the two environments for elderly subjects, including a decreased gait velocity (p = 0.0072), decreased cadence (p = 0.0051) and increased cycle time (p = 0.0051) in real-world settings. Importantly, the real-world environment increased the differences in several parameters between the young and elderly groups.ConclusionElderly test subjects walked differently in controlled lab settings compared to their real-world environments, which indicates the need to better understand natural walking patterns under ecologically valid conditions before clinically relevant conclusions can be drawn on a subject’s functional status. Moreover, the greater inter-group differences in real-world environments seem promising regarding the sensitive identification of subjects with indications of a walking disorder.

Incorporating Worker Awareness in the Generation of Hazard Proximity Warnings

Proximity warning systems for construction sites do not consider whether workers are already aware of the hazard prior to issuing warnings. This can generate redundant and distracting alarms that interfere with worker ability to adopt timely and appropriate avoidance measures; and cause alarm fatigue, which instigates workers to habitually disable the system or ignore the alarms; thereby increasing the risk of injury. Thus, this paper integrates the field-of-view of workers as a proxy for hazard awareness to develop an improved hazard proximity warning system for construction sites. The research first developed a rule-based model for the warning generation, which was followed by a virtual experiment to evaluate the integration of worker field-of-view in alarm generation. Based on these findings, an improved hazard proximity warning system incorporating worker field-of-view was developed for field applications that utilizes wearable inertial measurement units and localization sensors. The system’s effectiveness is illustrated through several case studies. This research provides a fresh perspective to the growing adoption of wearable sensors by incorporating the awareness of workers into the generation of hazard alarms. The proposed system is anticipated to reduce unnecessary and distracting alarms which can potentially lead to superior safety performance in construction.

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

Robust and accurate human activity recognition (HAR) systems are essential to many human-centric services within active assisted living and healthcare facilities. Traditional HAR systems mostly leverage a single sensing modality (e.g., either wearable, vision, or radio frequency sensing) combined with machine learning techniques to recognize human activities. Such unimodal HAR systems do not cope well with real-time changes in the environment. To overcome this limitation, new HAR systems that incorporate multiple sensing modalities are needed. Multiple diverse sensors can provide more accurate and complete information resulting in better recognition of the performed activities. This article presents WiWeHAR-a multimodal HAR system that uses combined Wi-Fi and wearable sensing modalities to simultaneously sense the performed activities. WiWeHAR makes use of standard Wi-Fi network interface cards to collect the channel state information (CSI) and a wearable inertial measurement unit (IMU) consisting of accelerometer, gyroscope, magnetometer sensors to collect the user's local body movements. We compute the time-variant mean Doppler shift (MDS) from the processed CSI data and magnitude from the inertial data for each sensor of the IMU. Thereafter, we separately extract various time- and frequency-domain features from the magnitude data and the MDS. We apply feature-level fusion to combine the extracted features, and finally supervised learning techniques are used to recognize the performed activities. We evaluate the performance of WiWeHAR by using a multimodal human activity data set, which was obtained from 9 participants. Each participant carried out four activities, such as walking, falling, sitting, and picking up an object from the floor. Our results indicate that the proposed multimodal WiWeHAR system outperforms the unimodal CSI, accelerometer, gyroscope, and magnetometer HAR systems and achieves an overall recognition accuracy of 99.6%-100%.

Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies.

Mobile gait analysis using wearable inertial measurement units (IMUs) provides valuable insights for the assessment of movement impairments in different neurological and musculoskeletal diseases, for example Parkinson's disease (PD). The increase in data volume due to arising long-term monitoring requires valid, robust and efficient analysis pipelines. In many studies an upstream detection of gait is therefore applied. However, current methods do not provide a robust way to successfully reject non-gait signals. Therefore, we developed a novel algorithm for the detection of gait from continuous inertial data of sensors worn at the feet. The algorithm is focused not only on a high sensitivity but also a high specificity for gait. Sliding windows of IMU signals recorded from the feet of PD patients were processed in the frequency domain. Gait was detected if the frequency spectrum contained specific patterns of harmonic frequencies. The approach was trained and evaluated on 150 clinical measurements containing standardized gait and cyclic movement tests. The detection reached a sensitivity of 0.98 and a specificity of 0.96 for the best sensor configuration (angular rate around the medio-lateral axis). On an independent validation data set including 203 unsupervised, semi-standardized gait tests, the algorithm achieved a sensitivity of 0.97. Our algorithm for the detection of gait from continuous IMU signals works reliably and showed promising results for the application in the context of free-living and non-standardized monitoring scenarios.

Gait quantification and visualization for digital healthcare

Gait abnormalities are common in clinical practice and there is a global imperative to improve technologies that facilitate their detection, evaluation, monitoring and management. Real time evaluation using digital technology supports the development of digital healthcare. Currently gait assessment relies on visual observation of structured clinical tests such as the “Timed Get up and Go Test.” Gold standard methods such as “Qualisys Motion Capture System” require sophisticated equipment in gait laboratories. These are not widely available due to expense, analysis time and requirement of trained technicians. Developing low cost, portable, easy to use digital technology is important to enable sophisticated assessment of gait at home or in clinics. Common measures for quantification of gait include symmetry angle, ratio and index. These measurements may be difficult to interpret by users as stand-alone values. To facilitate the evaluation and interpretation of locomotive information, a tool to visualize gait in real-time is proposed. The proposed tool consists of five approaches (1: Real-time dial visualization, 2: Visualization of individual leg time variation, 3: Visualization of both legs asymmetry, 4: Boxplot visualization, and 5: Evaluation considering all features). Results show that wearable Inertial Measurement Unit (IMU) can be used for extraction of objective gait features. This system opens possibilities for home-based assessment of gait without the requirement and expense of an elaborate laboratory setup and supports the development of digital healthcare.

Mobile manipulator control through gesture recognition using IMUs and Online Lazy Neighborhood Graph search

Gesture-based control potentially eliminates the need for wearisome physical controls and facilitates easy interaction between a human and a robot. At the same time, it is intuitive and enables a natural means of control. In this paper, we present and evaluate a framework for gesture recognition using four wearable Inertial Measurement Units (IMUs) to indirectly control a mobile robot. Six gestures involving different hand and arm motions are defined. A novel algorithm based on an Online Lazy Neighborhood Graph (OLNG) search is used to recognise and classify the gestures online. A software framework is developed to control a robotic platform through integrating our gesture recognition algorithm with a Robot Operating System (ROS), which is in turn used to trigger predefined robot behaviours. Experiments show that the framework is able to correctly detect and classify six different gestures in real time with average success rates of 81.61 % and 81.67 %, while keeping the false-positive rate low by designing and using only 126 training samples.

Magnetic Condition-Independent 3D Joint Angle Estimation Using Inertial Sensors and Kinematic Constraints.

In biomechanics, joint angle estimation using wearable inertial measurement units (IMUs) has been getting great popularity. However, magnetic disturbance issue is considered problematic as the disturbance can seriously degrade the accuracy of the estimated joint angles. This study proposes a magnetic condition-independent three-dimensional (3D) joint angle estimation method based on IMU signals. The proposed method is implemented in a sequential direction cosine matrix-based orientation Kalman filter (KF), which is composed of an attitude estimation KF followed by a heading estimation KF. In the heading estimation KF, an acceleration-level kinematic constraint from a spherical joint replaces the magnetometer signals for the correction procedure. Because the proposed method does not rely on the magnetometer, it is completely magnetic condition-independent and is not affected by the magnetic disturbance. For the averaged root mean squared errors of the three tests performed using a rigid two-link system, the proposed method produced 1.58°, while the conventional method with the magnetic disturbance compensation mechanism produced 5.38°, showing a higher accuracy of the proposed method in the magnetically disturbed conditions. Due to the independence of the proposed method from the magnetic condition, the proposed approach could be reliably applied in various fields that require robust 3D joint angle estimation through IMU signals in an unspecified arbitrary magnetic environment.

Autonomous Human Activity Classification From Wearable Multi-Modal Sensors

There has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. There has been relatively less work using wearable cameras, providing first-person or egocentric view, and even fewer approaches combining egocentric video with IMU data. Using only IMU data limits the variety and complexity of the activities that can be detected. For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is. To perform fine-grained activity classification, and to distinguish between activities that cannot be differentiated by only IMU data, we present an autonomous and robust method using data from both wearable cameras and IMUs. In contrast to convolutional neural network-based approaches, we propose to employ capsule networks to obtain features from egocentric video data. Moreover, Convolutional Long Short Term Memory framework is employed both on egocentric videos and IMU data to capture the temporal aspect of actions. We also propose a genetic algorithm-based approach to autonomously and systematically set various network parameters, rather than using manual settings. Experiments have been conducted to perform 9- and 26-label activity classification, and the proposed method, using autonomously set network parameters, has provided very promising results, achieving overall accuracies of 86.6% and 77.2%, respectively. The proposed approach, combining both modalities, also provides increased accuracy compared to using only egovision data and only IMU data.

Electronically augmented gait abnormality assessment following lower extremity trauma.

Background:Objective evaluation of patient outcomes has become an essential component of patient management. Along with patient-reported outcomes, performance-based measures (PBMs) such as gait analysis are an important part of this evaluation. The purpose of this study was to evaluate the validity of utilizing a wearable inertial measurement unit (IMU) in an outpatient clinic setting to assess its ability to provide clinically relevant data in patients with altered gait resulting from lower extremity trauma.Methods:Five orthopaedic trauma patients with varying degrees of gait pathologies were compared to 5 healthy control subjects. Kinematic data were simultaneously recorded by the IMU and a gold standard Vicon video motion analysis system (Vicon Motion Systems Ltd, Oxford, UK) during a modified 10-m walk test. Raw data captured by the IMU were directly compared to Vicon data. Additionally, 5 objective gait parameters were compared for controls and the 5 trauma patients.Results:The IMU data streams strongly correlated with Vicon data for measured variables used in the subsequent gait analysis: vertical acceleration, vertical displacement, pitch angular velocity, and roll angular velocity (Pearson r-value > 0.9 for all correlations). Quantitative kinematic data in post-trauma patients significantly differed from control data and correlated with observed gait pathology.Conclusions:When compared to the gold standard motion capture reference system (Vicon), an IMU can reliably and accurately measure clinically relevant gait parameters and differentiate between normal and pathologic gait patterns. This technology is easily integrated into clinical settings, requires minimal time, and represents a performance-based method for quantifiably assessing gait outcomes.Level of Evidence:Diagnostic Level 1.

Automated Detection of Multidirectional Compensatory Balance Reactions: A Step Towards Tracking Naturally Occurring Near Falls.

Falls are the leading cause of fatal and non-fatal injuries among seniors with serious and costly consequences. Laboratory evidence supports the view that impaired ability to execute compensatory balance reactions (CBRs) or near-falls is linked to an increased risk of falling. Therefore, as an alternative to the commonly used fall risk assessment methods examining spatial-temporal parameters of gait, this study focuses on the development of machine learning-based models to detect multidirectional CBRs using wearable inertial measurement units (IMUs). Random forest models were developed based upon the data captured by five wearable IMUs to 1) detect CBRs during normal gait, and 2) identify the type of CBR (eight different classes). A perturbation treadmill (PT) was employed to systematically elicit CBRs (i.e. PT-CBRs) during walking in different directions (e.g slip-like, trip-like, and medio-lateral) and amplitudes (e.g., low-, high-amplitude). We hypothesized that these PT-CBRs could simulate naturally-occurring CBRs (N-CBRs). Proof-of-concept testing in 9 young, healthy adults demonstrated accuracies of 96.60% and 80.64% for the PT-CBR detection and type identification models, respectively. Performance of the detection model was tested against a published dataset (IMUFD) simulating N-CBRs, including the most common types observed in older adults in long-term care facilities, which achieved sensitivity of 100%, but poor specificity. Adding normal gait data from IMUFD for training improved specificity, indicating treadmill walking alone is insufficient exemplar data. Perturbation treadmill combined with overground walking data is a suitable paradigm to collect training datasets of involuntary CBR events. These findings suggest that accurate detection of naturally-occurring CBRs is feasible, and supports further investigation of implementing a wearable sensor system to track naturally-occurring CBRs as a novel means of fall risk assessment.

CAPTURING REAL-WORLD LOSSES OF BALANCE AND RECOVERY RESPONSES IN OLDER ADULTS AT RISK FOR FALLS

Losses of balance (LOBs) such as trips can lead to falls in older adults; what actually happens during real-world LOBs is unclear. With 4 wearable inertial measurement units (IMUs), we recorded feet, trunk and wrist movements over 2 weeks. Using a wrist voice recorder to report the LOBs, we applied our IMU processing algorithms and reconstructed the full body LOB and recovery motions. We recruited 7 at-risk older adults (M=76 yrs) who reported 114 LOBs of which we reconstructed over 90%. Using a rating system, 52% of the LOBs involved a significant trip, stumble, recovery step, and/or large trunk motion. 25% involved double or stutter steps and smaller trunk motions. The other 23% had less striking associated motions. These data suggest that most, but not all, self-reported real world LOBs involve substantial postural destabilization and near falls. Analyses of the voice-recorded context under which the LOBs occurred are ongoing.

Influence of IMU position and orientation placement errors on ground reaction force estimation

Wearable inertial measurement units (IMU) have been proposed to estimate GRF outside of specialized laboratories, however the precise influence of sensor placement error on accuracy is unknown. We investigated the influence of IMU position and orientation placement errors on GRF estimation accuracy. Methods: Kinematic data from twelve healthy subjects based on marker trajectories were used to simulate 1848 combinations of sensor position placement errors (range ± 100 mm) and orientation placement errors (range ± 25°) across eight body segments (trunk, pelvis, left/right thighs, left/right shanks, and left/right feet) during normal walking trials for baseline cases when a single sensor was misplaced and for the extreme cases when all sensors were simultaneously misplaced. Three machine learning algorithms were used to estimate GRF for each placement error condition and compared with the no placement error condition to evaluate performance. Results: Position placement errors for a single misplaced IMU reduced vertical GRF (VGRF), medio-lateral GRF (MLGRF), and anterior-posterior GRF (APGRF) estimation accuracy by up to 1.1%, 2.0%, and 0.9%, respectively and for all eight simultaneously misplaced IMUs by up to 4.9%, 6.0%, and 4.3%, respectively. Orientation placement errors for a single misplaced IMU reduced VGRF, MLGRF, and APGRF estimation accuracy by up to 4.8%, 7.3%, and 1.5%, respectively and for all eight simultaneously misplaced IMUs by up to 20.8%, 23.4%, and 12.3%, respectively. Conclusion: IMU sensor misplacement, particularly orientation placement errors, can significantly reduce GRF estimation accuracy and thus measures should be taken to account for placement errors in implementations of GRF estimation via wearable IMUs.

Concurrent validity of a wearable IMU for objective assessments of functional movement quality and control of the lumbar spine

Inertial measurement units (IMUs) are being recognized in clinical and rehabilitation settings for their ability to assess movement-related disorders of the spine for better guidance of treatment-planning and tracking of recovery. This study evaluated the Mbientlab MetaMotionR IMUs, relative to Vicon motion capture equipment in measuring local dynamic stability of the spine (quantified using maximum finite-time Lyapunov exponent; λmax), lumbopelvic coordination (quantified using mean absolute relative phase; MARP), and intersegmental motor variability (quantified using deviation phase; DP) of lumbopelvic segments in 10 participants during 35 cycles of repetitive spine flexion–extension (FE). Intraclass correlations were strong between systems when using both the FE angle time-series and the sum of squares (SS) time-series to measure local dynamic stability (0.807 ≤ICC2,1λmax,FE ≤ 0.919; 0.738 ≤ ICC2,1λmax,SS ≤ 0.868), sagittal-plane lumbopelvic coordination (0.961 ≤ICC2,1MARP ≤ 0.963), and sagittal-plane lumbopelvic variability (0.961 ≤ICC2,1DP ≤ 0.963). It was concluded that the MetaMotionR IMUs can be reliably used for measuring features associated with spine movement quality and motor control during a repetitive FE task. Future work will assess the reliability of sensor placement, performance during multi-directional movements, and ability to discern clinical and healthy populations based on assessment of movement quality and control.

Upper limb joint kinematics using wearable magnetic and inertial measurement units: an anatomical calibration procedure based on bony landmark identification

The estimate of a consistent and clinically meaningful joint kinematics using wearable inertial and magnetic sensors requires a sensor-to-segment coordinate system calibration. State-of-the-art calibration procedures for the upper limb are based on functional movements and/or pre-determined postures, which are difficult to implement in subjects that have impaired mobility or are bedridden in acute units. The aim of this study was to develop and validate an alternative calibration procedure based on the direct identification of palpable anatomical landmarks (ALs) for an inertial and magnetic sensor-based upper limb movement analysis protocol. The proposed calibration procedure provides an estimate of three-dimensional shoulder/elbow angular kinematics and the linear trajectory of the wrist according to the standards proposed by the International Society of Biomechanics. The validity of the method was assessed against a camera-based optoelectronic system during uniaxial joint rotations and a reach-to-grasp task. Joint angular kinematics was found as characterised by a low-biased range of motion (<−2.6°), a low root mean square deviation (RMSD) (<4.4°) and a high waveform similarity coefficient (R2 > 0.995) with respect to the gold standard. Except for the cranio–caudal direction, the linear trajectory of the wrist was characterised by a low-biased range of motion (<11 mm) together with a low RMSD (8 mm) and high waveform similarity (R2 > 0.968). The proposed method enabled the estimation of reliable joint kinematics without requiring any active involvement of the patient during the calibration procedure, complying with the metrological standards and requirements of clinical movement analysis.