Continuously monitoring shoulder motion after total shoulder arthroplasty: maximum elevation and time spent above 90° of elevation are critical metrics to monitor

BACKGROUND

Traditional clinical shoulder range-of-motion (ROM) measurement methods (ie, goniometry) have limitations assessing ROM in total shoulder arthroplasty (TSA) patients. Inertial measurement units (IMUs) are superior; however, further work is needed using IMUs to longitudinally assess shoulder ROM before TSA and throughout post-TSA rehabilitation. Accordingly, the study aims were to prospectively capture shoulder elevation in TSA patients and to compare the results with healthy controls. We hypothesized that patients would have reduced maximum elevation before TSA compared with controls but would have improved ROM after TSA.

METHODS

A validated IMU-based shoulder elevation quantification method was used to continuously monitor 10 healthy individuals (4 men and 6 women; mean age, 69 ± 20 years) without shoulder pathology and 10 TSA patients (6 men and 4 women; mean age, 70 ± 8 years). Controls wore IMUs for 1 week. Patients wore IMUs for 1 week before TSA, for 6 weeks at 3 months after TSA, and for 1 week at 1 year after TSA. Shoulder elevation was calculated continuously, broken into 5° angle "bins" (0°-5°, 5°-10°, and so on), and converted to percentages. The main outcome measures were binned movement percentage, maximum elevation, and average elevation. Patient-reported outcome measures and goniometric ROM were also captured.

RESULTS

No demographic differences were noted between the cohorts. Average elevation was not different between the cohorts at any time. Control maximum elevation was greater than pre-TSA and post-TSA week 1 and week 2 values. Time under 30° and time above 90° were equal between the cohorts before TSA. After TSA, patients showed decreased time under 30° and increased time above 90°.

DISCUSSION

This study demonstrates that acute and chronic recovery after TSA can be assessed via maximum elevation and time above 90°, respectively. These results inform how healthy individuals and patients use their shoulders before and after TSA.

Polymer Optical Fiber Sensors in Healthcare Applications: A Comprehensive Review

Advances in medicine and improvements in life quality has led to an increase in the life expectancy of the general population. An ageing world population have placed demands on the use of assistive technology and, in particular, towards novel healthcare devices and sensors. Besides the electromagnetic field immunity, polymer optical fiber (POF) sensors have additional advantages due to their material features such as high flexibility, lower Young’s modulus (enabling high sensitivity for mechanical parameters), higher elastic limits, and impact resistance. Such advantages are well-aligned with the instrumentation requirements of many healthcare devices and in movement analysis. Aiming at these advantages, this review paper presents the state-of-the-art developments of POF sensors for healthcare applications. A plethora of healthcare applications are discussed, which include movement analysis, physiological parameters monitoring, instrumented insoles, as well as instrumentation of healthcare robotic devices such as exoskeletons, smart walkers, actuators, prostheses, and orthosis. This review paper shows the feasibility of using POF sensors in healthcare applications and, due to the aforementioned advantages, it is possible to envisage a further widespread use of such sensors in this research field in the next few years.

Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review

Multibody kinematics optimization (MKO) aims to reduce soft tissue artefact (STA) and is a key step in musculoskeletal modeling. The objective of this review was to identify the numerical methods, their validation and performance for the estimation of the human joint kinematics using MKO. Seventy-four papers were extracted from a systematized search in five databases and cross-referencing. Model-derived kinematics were obtained using either constrained optimization or Kalman filtering to minimize the difference between measured (i.e., by skin markers, electromagnetic or inertial sensors) and model-derived positions and/or orientations. While hinge, universal, and spherical joints prevail, advanced models (e.g., parallel and four-bar mechanisms, elastic joint) have been introduced, mainly for the knee and shoulder joints. Models and methods were evaluated using: (i) simulated data based, however, on oversimplified STA and joint models; (ii) reconstruction residual errors, ranging from 4 mm to 40 mm; (iii) sensitivity analyses which highlighted the effect (up to 36 deg and 12 mm) of model geometrical parameters, joint models, and computational methods; (iv) comparison with other approaches (i.e., single body kinematics optimization and nonoptimized kinematics); (v) repeatability studies that showed low intra- and inter-observer variability; and (vi) validation against ground-truth bone kinematics (with errors between 1 deg and 22 deg for tibiofemoral rotations and between 3 deg and 10 deg for glenohumeral rotations). Moreover, MKO was applied to various movements (e.g., walking, running, arm elevation). Additional validations, especially for the upper limb, should be undertaken and we recommend a more systematic approach for the evaluation of MKO. In addition, further model development, scaling, and personalization methods are required to better estimate the secondary degrees-of-freedom (DoF).

Magnetometer-Based Drift Correction During Rest inIMU Arm Motion Tracking

Real-time motion capture of the human arm in the home environment has many usecases, such as video game and therapy applications. The required tracking can be based onoff-the-shelf Inertial Measurement Units (IMUs) with integrated three-axis accelerometers, gyroscopes,and magnetometers. However, this usually requires a homogeneous magnetic field to correctfor orientation drift, which is often not available inside buildings. In this paper, RPMC (RestPose Magnetometer-based drift Correction), a novel method that is robust to long term drift inenvironments with inhomogeneous magnetic fields, is presented. The sensor orientation is estimatedby integrating the angular velocity measured by the gyroscope and correcting drift around the pitchand roll axes with the acceleration information. This commonly leads to short term drift aroundthe gravitational axis. Here, during the calibration phase, the local magnetic field direction for eachsensor, and its orientation relative to the inertial frame, are recorded in a rest pose. It is assumed thatarm movements in free space are exhausting and require regular rest. A set of rules is used to detectwhen the user has returned to the rest pose, to then correct for the drift that has occurred with themagnetometer. Optical validations demonstrated accurate (root mean square error RMS = 6.1), lowlatency (61 ms) tracking of the user's wrist orientation, in real time, for a full hour of arm movements.The reduction in error relative to three alternative methods implemented for comparison was between82.5% and 90.7% for the same movement and environment. Therefore, the proposed arm trackingmethod allows for the correction of orientation drift in an inhomogeneous magnetic field by exploitingthe user's need for frequent rest.

A Comparative Study of Markerless Systems Based on Color-Depth Cameras, Polymer Optical Fiber Curvature Sensors, and Inertial Measurement Units: Towards Increasing the Accuracy in Joint Angle Estimation

This paper presents a comparison between a multiple red green blue-depth (RGB-D) vision system, an intensity variation-based polymer optical fiber (POF) sensor, and inertial measurement units (IMUs) for human joint angle estimation and movement analysis. This systematic comparison aims to study the trade-off between the non-invasive feature of a vision system and its accuracy with wearable technologies for joint angle measurements. The multiple RGB-D vision system is composed of two camera-based sensors, in which a sensor fusion algorithm is employed to mitigate occlusion and out-range issues commonly reported in such systems. Two wearable sensors were employed for the comparison of angle estimation: (i) a POF curvature sensor to measure 1-DOF angle; and (ii) a commercially available IMUs MTw Awinda from Xsens. A protocol to evaluate elbow joints of 11 healthy volunteers was implemented and the comparison of the three systems was presented using the correlation coefficient and the root mean squared error (RMSE). Moreover, a novel approach for angle correction of markerless camera-based systems is proposed here to minimize the errors on the sagittal plane. Results show a correlation coefficient up to 0.99 between the sensors with a RMSE of 4.90 ∘ , which represents a two-fold reduction when compared with the uncompensated results (10.42 ∘ ). Thus, the RGB-D system with the proposed technique is an attractive non-invasive and low-cost option for joint angle assessment. The authors envisage the proposed vision system as a valuable tool for the development of game-based interactive environments and for assistance of healthcare professionals on the generation of functional parameters during motion analysis in physical training and therapy.

Wrap-Around Wearable Coils for Seamless Monitoring of Joint Flexion

OBJECTIVE

We introduce and validate a new class of wearable coils that seamlessly monitor joint flexion in the individual's natural environment while overcoming shortcomings in state-of-the-art.

METHODS

Our approach relies on Faraday's law of induction and employs wrap-around transmit and receive coils that get angularly misaligned as the joint flexes.

RESULTS

Simulation and in vitro measurement results for both copper and e-thread coils are in excellent agreement. As a proof-of-concept, a cylindrical arm model is considered and feasibility of monitoring the 0°-130° range of motion is confirmed. The operation frequency of 34 MHz is identified as optimal, bringing forward reduced power requirements, enhanced angular resolution, and extreme robustness to tissue dielectric property variations. Performance benchmarking versus state-of-the-art inertial measurement units shows equivalent or superior performance, particularly for flexion angles greater than 20°. Design guidelines and safety considerations are also explored.

CONCLUSION

Contrary to "gold-standard" camera-based motion capture, the reported approach is not restricted to contrived environments. Concurrently, it does not suffer from integration drift (unlike inertial measurement units), it does not require line-of-sight (unlike time-of-flight sensors), and it does not restrict natural joint movement (unlike bending sensors).

SIGNIFICANCE

The reported approach is envisioned to be seamlessly integrated into garments and, eventually, redefine the way joint flexion is monitored at present. This promises unprecedented opportunities for rehabilitation, sports, gestural interaction, and more.

Polymer Optical Fiber Goniometer: A New Portable, Low Cost and Reliable Sensor for Joint Analysis

The quantitative measurement of an articular motion is an important indicator of its functional state and for clinical and pathology diagnoses. Joint angle evaluation techniques can be applied to improve sports performance and provide feedback information for prostheses control. Polymer optical fiber (POF) sensors are presented as a novel method to evaluate joint angles, because they are compact, lightweight, flexible and immune to electromagnetic interference. This study aimed to characterize and implement a new portable and wearable system to measure angles based on a POF curvature sensor. This study also aimed to present the system performance in bench tests and in the measurement of the elbow joint in ten participants, comparing the results with a consolidated resistive goniometer. Results showed high repeatability of sensors between cycles and high similarity of behavior with the potentiometer, with the advantage of being more ergonomic. The proposed sensor presented errors comparable to the literature and showed some advantages over other goniometers, such as the inertial measurement unit (IMU) sensor and over other types of POF sensors. This demonstrates its applicability for joint angle evaluation.

Measurement of Upper Limb Range of Motion Using Wearable Sensors: A Systematic Review

Background

Wearable sensors are portable measurement tools that are becoming increasingly popular for the measurement of joint angle in the upper limb. With many brands emerging on the market, each with variations in hardware and protocols, evidence to inform selection and application is needed. Therefore, the objectives of this review were related to the use of wearable sensors to calculate upper limb joint angle. We aimed to describe (i) the characteristics of commercial and custom wearable sensors, (ii) the populations for whom researchers have adopted wearable sensors, and (iii) their established psychometric properties.

Methods

A systematic review of literature was undertaken using the following data bases: MEDLINE, EMBASE, CINAHL, Web of Science, SPORTDiscus, IEEE, and Scopus. Studies were eligible if they met the following criteria: (i) involved humans and/or robotic devices, (ii) involved the application or simulation of wearable sensors on the upper limb, and (iii) calculated a joint angle.

Results

Of 2191 records identified, 66 met the inclusion criteria. Eight studies compared wearable sensors to a robotic device and 22 studies compared to a motion analysis system. Commercial (n = 13) and custom (n = 7) wearable sensors were identified, each with variations in placement, calibration methods, and fusion algorithms, which were demonstrated to influence accuracy.

Conclusion

Wearable sensors have potential as viable instruments for measurement of joint angle in the upper limb during active movement. Currently, customised application (i.e. calibration and angle calculation methods) is required to achieve sufficient accuracy (error <  5°). Additional research and standardisation is required to guide clinical application.

Trial Registration

This systematic review was registered with PROSPERO (CRD42017059935).

A survey of human shoulder functional kinematic representations

In this survey, we review the field of human shoulder functional kinematic representations. The central question of this review is to evaluate whether the current approaches in shoulder kinematics can meet the high-reliability computational challenge. This challenge is posed by applications such as robot-assisted rehabilitation. Currently, the role of kinematic representations in such applications has been mostly overlooked. Therefore, we have systematically searched and summarised the existing literature on shoulder kinematics. The shoulder is an important functional joint, and its large range of motion (ROM) poses several mathematical and practical challenges. Frequently, in kinematic analysis, the role of the shoulder articulation is approximated to a ball-and-socket joint. Following the high-reliability computational challenge, our review challenges this inappropriate use of reductionism. Therefore, we propose that this challenge could be met by kinematic representations, that are redundant, that use an active interpretation and that emphasise on functional understanding.

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.

A Limb Tracking Platform for Tele-Rehabilitation

The adoption of motor-rehabilitative therapies is highly demanded in a society where the average age of the population is constantly increasing. A recent trend to contain costs while providing high quality of healthcare services is to foster the adoption of self-care procedures, performed primarily in patients’ environments rather than in hospitals or healthcare structures, especially in the case of intensive and chronic patients’ rehabilitation.

This work presents a platform to enhance limb functional recovery through telerehabilitation sessions. It relies on a sensing system based on inertial sensors and data fusion algorithms, a module to provide bio-feedback tailored to the users, and a module dedicated to the physicians’ practices. The system design had to face several cyber-physical challenges due to the tight interaction between patient and sensors. For instance, integrating the body kinematics into the sensory processing improved the precision of measurements, simplified the calibration procedure, and made it possible to generate bio-feedback signals. The precision of the proposed system is presented through a set of experiments, showing a resolution below one degree in monitoring joint angles. A validation of the proposed solution has been performed through a medical trial on 50 patients affected by osteo-articular diseases.

The presented framework has been designed to operate in other application fields, such as neurological rehabilitation (e.g., Parkinson, Stroke, etc.), sports training, and fitness activities.

Design considerations, analysis, and application of a low-cost, fully portable, wearable polymer optical fiber curvature sensor

This paper presents the development of a low-cost, fully portable, wearable sensor for joint angle assessment based on a polymer optical fiber (POF) curvature sensor. The mechanical support configurations as well as the fiber length are analyzed to obtain a sensor with lower hysteresis and higher sensitivity and linearity. In addition, the annealing is made in the fiber to further reduce the sensor errors, and an analysis to obtain the sensor cross-sensitivity with respect to temperature and relative humidity is performed. Finally, a viscoelastic-based compensation technique is applied on the proposed wearable sensor not only to reduce its hysteresis and errors, but also to increase the sensor linearity. The sensor is validated on flexion and extension cycles with different angular velocities. Results show that the proposed sensor presents root mean squared errors of about 1.5° and mean hysteresis of about 1%. The wearable POF curvature sensor was applied on the angle measurement of an elbow joint during flexion and extension cycles and on the knee during the gait cycle, where high repeatability and low errors also were found.

Polymer Optical Fiber Bragg Gratings in CYTOP Fibers for Angle Measurement with Dynamic Compensation

This paper demonstrates the use of polymer optical fiber Bragg gratings (POFBGs) for angle measurements over a range of different oscillatory frequencies. The POFBGs are inscribed in low-loss, cyclic transparent amorphous fluoropolymers (CYTOP) and are imprinted using the direct-write, plane-by-plane femtosecond laser inscription method. As the polymer has a viscoelastic response and given that the Young's modulus depends on the oscillatory frequency, a compensation technique for sensor frequency cross-sensitivity and hysteresis is proposed and verified. Results show that the proposed compensation technique is able to provide a root mean squared error (RMSE) reduction of 44%, and a RMSE as low as 2.20° was obtained when compared with a reference potentiometer. The hysteresis reduction provided by the proposed technique is 55%, with hysteresis <0.01. The results presented in this paper can pave the way for movement analysis with POFBG providing higher sensitivity and low hysteresis over a large range of motion frequencies.

EquiMoves: A Wireless Networked Inertial Measurement System for Objective Examination of Horse Gait

In this paper, we describe and validate the EquiMoves system, which aims to support equine veterinarians in assessing lameness and gait performance in horses. The system works by capturing horse motion from up to eight synchronized wireless inertial measurement units. It can be used in various equine gait modes, and analyzes both upper-body and limb movements. The validation against an optical motion capture system is based on a Bland-Altman analysis that illustrates the agreement between the two systems. The sagittal kinematic results (protraction, retraction, and sagittal range of motion) show limits of agreement of ± 2.3 degrees and an absolute bias of 0.3 degrees in the worst case. The coronal kinematic results (adduction, abduction, and coronal range of motion) show limits of agreement of - 8.8 and 8.1 degrees, and an absolute bias of 0.4 degrees in the worst case. The worse coronal kinematic results are most likely caused by the optical system setup (depth perception difficulty and suboptimal marker placement). The upper-body symmetry results show no significant bias in the agreement between the two systems; in most cases, the agreement is within ±5 mm. On a trial-level basis, the limits of agreement for withers and sacrum are within ±2 mm, meaning that the system can properly quantify motion asymmetry. Overall, the bias for all symmetry-related results is less than 1 mm, which is important for reproducibility and further comparison to other systems.

Accuracy of angular displacements and velocities from inertial-based inclinometers

The objective of this study was to evaluate the accuracy of various sensor fusion algorithms for measuring upper arm elevation relative to gravity (i.e., angular displacement and velocity summary measures) across different motion speeds. Thirteen participants completed a cyclic, short duration, arm-intensive work task that involved transfering wooden dowels at three work rates (slow, medium, fast). Angular displacement and velocity measurements of upper arm elevation were simultaneously measured using an inertial measurement unit (IMU) and an optical motion capture (OMC) system. Results indicated that IMU-based inclinometer solutions can reduce root-mean-square errors in comparison to accelerometer-based inclination estimates by as much as 87%, depending on the work rate and sensor fusion approach applied. The findings suggest that IMU-based inclinometers can substantially improve inclinometer accuracy in comparison to traditional accelerometer-based inclinometers. Ergonomists may use the non-proprietary sensor fusion algorithms provided here to more accurately estimate upper arm elevation.

Statistical Prediction of Hand Force Exertion Levels in a Simulated Push Task using Posture Kinematics

This study explored the use of body posture kinematics derived from wearable inertial sensors to estimate force exertion levels in a two-handed isometric pushing and pulling task. A prediction model was developed grounded on the hypothesis that body postures predictably change depending on the magnitude of the exerted force. Five body postural angles, viz., torso flexion, pelvis flexion, lumbar flexion, hip flexion, and upper arm inclination, collected from 15 male participants performing simulated isometric pushing and pulling tasks in the laboratory were used as predictor variables in a statistical model to estimate handle height (shoulder vs. hip) and force intensity level (low vs. high). Individual anthropometric and strength measurements were also included as predictors. A Random Forest algorithm implemented in a two-stage hierarchy correctly classified 77.2% of the handle height and force intensity levels. Results represent early work in coupling unobtrusive, wearable instrumentation with statistical learning techniques to model occupational activities and exposures to biomechanical risk factors in situ.

Wearable Inertial Sensors Allow for Quantitative Assessment of Shoulder and Elbow Kinematics in a Cadaveric Knee Arthroscopy Model

PURPOSE

To develop a model using wearable inertial sensors to assess the performance of orthopaedic residents while performing a diagnostic knee arthroscopy.

METHODS

Fourteen subjects performed a diagnostic arthroscopy on a cadaveric right knee. Participants were divided into novices (5 postgraduate year 3 residents), intermediates (5 postgraduate year 4 residents), and experts (4 faculty) based on experience. Arm movement data were collected by inertial measurement units (Opal sensors) by securing 2 sensors to each upper extremity (dorsal forearm and lateral arm) and 2 sensors to the trunk (sternum and lumbar spine). Kinematics of the elbow and shoulder joints were calculated from the inertial data by biomechanical modeling based on a sequence of links connected by joints. Range of motion required to complete the procedure was calculated for each group. Histograms were used to compare the distribution of joint positions for an expert, intermediate, and novice.

RESULTS

For both the right and left upper extremities, skill level corresponded well with shoulder abduction-adduction and elbow prono-supination. Novices required on average 17.2° more motion in the right shoulder abduction-adduction plane than experts to complete the diagnostic arthroscopy (P = .03). For right elbow prono-supination (probe hand), novices required on average 23.7° more motion than experts to complete the procedure (P = .03). Histogram data showed novices had markedly more variability in shoulder abduction-adduction and elbow prono-supination compared with the other groups.

CONCLUSIONS

Our data show wearable inertial sensors can measure joint kinematics during diagnostic knee arthroscopy. Range-of-motion data in the shoulder and elbow correlated inversely with arthroscopic experience. Motion pattern-based analysis shows promise as a metric of resident skill acquisition and development in arthroscopy.

CLINICAL RELEVANCE

Wearable inertial sensors show promise as metrics of arthroscopic skill acquisition among residents.

A Nonlinear Dynamics-Based Estimator for Functional Electrical Stimulation: Preliminary Results From Lower-Leg Extension Experiments

Miniature inertial measurement units (IMUs) are wearable sensors that measure limb segment or joint angles during dynamic movements. However, IMUs are generally prone to drift, external magnetic interference, and measurement noise. This paper presents a new class of nonlinear state estimation technique called state-dependent coefficient (SDC) estimation to accurately predict joint angles from IMU measurements. The SDC estimation method uses limb dynamics, instead of limb kinematics, to estimate the limb state. Importantly, the nonlinear limb dynamic model is formulated into state-dependent matrices that facilitate the estimator design without performing a Jacobian linearization. The estimation method is experimentally demonstrated to predict knee joint angle measurements during functional electrical stimulation of the quadriceps muscle. The nonlinear knee musculoskeletal model was identified through a series of experiments. The SDC estimator was then compared with an extended kalman filter (EKF), which uses a Jacobian linearization and a rotation matrix method, which uses a kinematic model instead of the dynamic model. Each estimator's performance was evaluated against the true value of the joint angle, which was measured through a rotary encoder. The experimental results showed that the SDC estimator, the rotation matrix method, and EKF had root mean square errors of 2.70°, 2.86°, and 4.42°, respectively. Our preliminary experimental results show the new estimator's advantage over the EKF method but a slight advantage over the rotation matrix method. However, the information from the dynamic model allows the SDC method to use only one IMU to measure the knee angle compared with the rotation matrix method that uses two IMUs to estimate the angle.

Validation of Inertial Measurement Units for Upper Body Kinematics

The purpose of this study was to validate a commercially available inertial measurement unit (IMU) system against a standard lab-based motion capture system for the measurement of shoulder elevation, elbow flexion, trunk flexion/extension, and neck flexion/extension kinematics. The validation analyses were applied to 6 surgical faculty members performing a standard, simulated surgical training task that mimics minimally invasive surgery. Three-dimensional joint kinematics were simultaneously recorded by an optical motion capture system and an IMU system with 6 sensors placed on the head, chest, and bilateral upper and lower arms. The sensor-to-segment axes alignment was accomplished manually. The IMU neck and trunk IMU flexion/extension angles were accurate to within 2.9 ± 0.9 degrees and 1.6 ± 1.1°, respectively. The IMU shoulder elevation measure was accurate to within 6.8 ± 2.7° and the elbow flexion measure was accurate to within 8.2 ± 2.8°. In the Bland-Altman analyses, there were no significant systematic errors present; however, there was a significant inversely proportional error across all joints. As the gold standard measurement increased, the IMU underestimated the magnitude of the joint angle. This study reports acceptable accuracy of a commercially available IMU system; however, results should be interpreted as protocol specific.

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).

Inertial and time-of-arrival ranging sensor fusion

Wearable devices with embedded kinematic sensors including triaxial accelerometers, gyroscopes, and magnetometers are becoming widely used in applications for tracking human movement in domains that include sports, motion gaming, medicine, and wellness. The kinematic sensors can be used to estimate orientation, but can only estimate changes in position over short periods of time. We developed a prototype sensor that includes ultra wideband ranging sensors and kinematic sensors to determine the feasibility of fusing the two sensor technologies to estimate both orientation and position. We used a state space model and applied the unscented Kalman filter to fuse the sensor information. Our results demonstrate that it is possible to estimate orientation and position with less error than is possible with either sensor technology alone. In our experiment we obtained a position root mean square error of 5.2cm and orientation error of 4.8° over a 15min recording.

The sensitivity of shoulder muscle and joint force predictions to changes in joint kinematics: A Monte-Carlo analysis

Kinematics of the shoulder girdle obtained from non-invasive measurement systems such as video motion analysis, accelerometers and magnetic tracking sensors has been shown to be adversely affected by instrumentation measurement errors and skin motion artefact. The degree to which musculoskeletal model calculations of shoulder muscle and joint loading are influenced by variations in joint kinematics is currently not well understood. A three-dimensional musculoskeletal model of the upper limb was used to evaluate the sensitivity of shoulder muscle and joint force. Monte-Carlo analyses were performed by randomly perturbing scapular and humeral joint coordinates during abduction and flexion. Muscle and joint force calculations were generally most sensitive to changes in the kinematics of the humerus in elevation and of the scapula in medial-lateral rotation, and were least sensitive to changes in humerus plane of elevation and scapula protraction-retraction. Overall model sensitivity was greater during abduction than flexion, and the influence of specific kinematics perturbations varied from muscle to muscle. In general, muscles that generated greater force, such as the middle deltoid and subscapularis, were more sensitive to changes in shoulder kinematics. This study suggests that musculoskeletal model sensitivity to changes in kinematics is task-specific, and varies depending on the plane of motion. Calculations of shoulder muscle and joint function depend on reliable humeral and scapula motion data, particularly that of humeral elevation and scapula medial-lateral rotation. The findings in this study have implications for the use of kinematic data in musculoskeletal model development and simulations.

Measurement properties of a new wireless electrogoniometer for quantifying spasticity during the pendulum test in ARSACS patients

INTRODUCTION

Autosomal recessive spastic ataxia of Charlevoix/Saguenay (ARSACS) is a neuromuscular disorder that induces spasticity in lower limbs. The Wartenberg pendulum test is a classical method of assessing lower limb spasticity based on the dynamics of the pendular leg motion. However, in its original form, this test only provides subjective results and do not allow accurate assessment of spasticity.

METHODS

Thirteen ARSACS patients were assessed using a new wireless electrogoniometer to measure spasticity by quantifying oscillation amplitudes and relaxation indices during the Wartenburg pendulum test. The validity of the instrument was evaluated by comparing its measurements to a known precise goniometer whereas discriminant validity was evaluated by comparing healthy participants and ARSACS patients. Reliability was measured using intraclass correlation (ICC) between pendulum test scores obtained at different moments in time.

RESULTS

Data from different tests show that the proposed device is accurate (standard error of measurement of 0.0005°), discriminates healthy and ARSACS patients (most variables have p=0.00) and provides repeatable results (significant ICC usually higher than 0.64 and p<0.05).

DISCUSSION

The proposed tool allows the clinician to analyze pendulum oscillation amplitudes and ratios and thus, provide an index of spasticity for the patients affected by ARSACS. This is important as the original procedure is only evaluated visually and the progression cannot be detected until the condition changes drastically. Thus, the system proposed meets the requirements of being useful, precise and user-friendly in the evaluation of patients in a research as well as a clinical environment.

Gait and Functional Mobility Deficits in Fragile X-Associated Tremor/Ataxia Syndrome

Fragile X-associated tremor/ataxia syndrome (FXTAS) results from a "premutation" (PM) size CGG repeat expansion in the fragile X mental retardation 1 (FMR1) gene. Cerebellar gait ataxia is the primary feature in some FXTAS patients causing progressive disability. However, no studies have quantitatively characterized gait and mobility deficits in FXTAS. We performed quantitative gait and mobility analysis in seven FMR1 PM carriers with FXTAS and ataxia, six PM carriers without FXTAS, and 18 age-matched controls. We studied four independent gait domains, trunk range of motion (ROM), and movement transitions using an instrumented Timed Up and Go (i-TUG). We correlated these outcome measures with FMR1 molecular variables and clinical severity scales. PM carriers with FXTAS were globally impaired in every gait performance domain except trunk ROM compared to controls. These included total i-TUG duration, stride velocity, gait cycle time, cadence, double-limb support and swing phase times, turn duration, step time before turn, and turn-to-sit duration, and increased gait variability on several measures. Carriers without FXTAS did not differ from controls on any parameters, but double-limb support time was close to significance. Balance and disability scales correlated with multiple gait and movement transition parameters, while the FXTAS Rating Scale did not. This is the first study to quantitatively examine gait and movement transitions in FXTAS patients. Gait characteristics were consistent with those from previous cohorts with cerebellar ataxia. Sensitive measures like the i-TUG may help determine efficacy of interventions, characterize disease progression, and provide early markers of disease in FXTAS.

Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking

The wearable inertial/magnetic sensor based human motion analysis plays an important role in many biomedical applications, such as physical therapy, gait analysis and rehabilitation. One of the main challenges for the lower body bio-motion analysis is how to reliably provide position estimations of human subject during walking. In this paper, we propose a particle filter based human position estimation method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity update (ZUPT), but also applies map information to further correct the acceleration double integration drift and thus improve estimation accuracy. In the proposed method, a simple stance phase detector is designed to identify the stance phase of a gait cycle based on gyroscope measurements. For the non-stance phase during a gait cycle, an acceleration control variable derived from ZUPT information is introduced in the process model, while vector map information is taken as binary pseudo-measurements to further enhance position estimation accuracy and reduce uncertainty of walking trajectories. A particle filter is then designed to fuse ZUPT information and binary pseudo-measurements together. The proposed human position estimation method has been evaluated with closed-loop walking experiments in indoor and outdoor environments. Results of comparison study have illustrated the effectiveness of the proposed method for application scenarios with useful map information.

Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation

This paper proposes a method to enable the use of non-intrusive, small, wearable, and wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic-EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during pose estimation. The canonical dynamical system models the motion as a periodic signal. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features, such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the extended Kalman filter in simulation, on healthy participant data, and stroke patient data. For the healthy participant marching dataset, the Rhythmic-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37%, respectively, estimates joint angles with 2.4° root mean squared error, and segments the motion into repetitions with 96% accuracy.

Model-based extended quaternion Kalman filter to inertial orientation tracking of arbitrary kinematic chains

Inertial orientation tracking is still an area of active research, especially in the context of out-door, real-time, human motion capture. Existing systems either propose loosely coupled tracking approaches where each segment is considered independently, taking the resulting drawbacks into account, or tightly coupled solutions that are limited to a fixed chain with few segments. Such solutions have no flexibility to change the skeleton structure, are dedicated to a specific set of joints, and have high computational complexity. This paper describes the proposal of a new model-based extended quaternion Kalman filter that allows for estimation of orientation based on outputs from the inertial measurements unit sensors. The filter considers interdependencies resulting from the construction of the kinematic chain so that the orientation estimation is more accurate. The proposed solution is a universal filter that does not predetermine the degree of freedom at the connections between segments of the model. To validation the motion of 3-segments single link pendulum captured by optical motion capture system is used. The next step in the research will be to use this method for inertial motion capture with a human skeleton model.

The feasibility of shoulder motion tracking during activities of daily living using inertial measurement units

Measurements of shoulder kinematics during activities of daily living (ADL) can be used to evaluate patient function before and after treatment and help define device testing conditions. The purpose of this study was to demonstrate the feasibility of using wearable inertial measurement units (IMUs) to track shoulder joint angles while performing actual ADLs outside of laboratory simulations. IMU data of 5 subjects with normal shoulders was collected for 4h at the subjects' workplace and up to 4h off-work. An Unscented Kalman Filter (UKF) enhanced with gyroscope bias modeling and zero velocity updates demonstrated an accuracy of about 2° and was used to estimate relative upper arm angles from the IMU data. The overall averaged 95th percentile angles were: flexion 128.8°, abduction 128.4°, and external rotation 69.5°. These peaks angles are similar to other investigator's reports using laboratory simulations of ADLs measured with optical and electromagnetic technologies. Additionally, with a Fourier transform the 50th percentile frequency was determined and used to extrapolate the typical number of arm cycles in a 10year period to be 649,000. Application of the UKF with the additional drift correction made substantial improvements in shoulder tracking performance and this feasibility data suggests that IMUs with the UKF are suitable for extended use outside of laboratory settings. The data provides a novel description of arm motion during ADLs including an estimate for the 10 year cycle count of upper arm motion.

Analytical Inverse Kinematic Resolution of a Redundant Exoskeleton for Upper-Limb Rehabilitation

Robot-assisted therapy has played a significant role in helping the disabled patients to restore motor functions. In this paper, a redundant exoskeleton is developed for upper-limb rehabilitation. An analytical methodology for obtaining the inverse kinematic solution of the exoskeleton is presented to provide synchronized movement with patients and ensure natural human–robot interaction. To mathematically express the redundancy problem, the swivel angle of elbow is introduced as an additional parameter to specify the human arm congratulation with a predefined wrist location. A kinematic criterion is proposed to determine the swivel angle by imitating the natural reflexive reaction of human arm. The effectiveness of the proposed strategy is experimentally evaluated via four representative types of upper-limb motion tasks. During the experiments, the actual kinematic data of human arm is collected by utilizing an articulated motion capture system integrated with inertial sensors and, after that, compared to the estimation results generated by the proposed redundancy resolution. The experimental results indicate that the kinematic criterion of swivel angle is suitable to describe the free reaching movement without additional constraints. Moreover, with the estimated swivel angles, the root mean square errors between the actual and calculated joint angles are normally less than 8[Formula: see text].

Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis

The potential of inertial measurement units (IMUs) for ergonomics applications appears promising. However, previous IMUs validation studies have been incomplete regarding aspects of joints analysed, complexity of movements and duration of trials. The objective was to determine the technological error and biomechanical model differences between IMUs and an optoelectronic system and evaluate the effect of task complexity and duration. Whole-body kinematics from 12 participants was recorded simultaneously with a full-body Xsens system where an Optotrak cluster was fixed on every IMU. Short functional movements and long manual material handling tasks were performed and joint angles were compared between the two systems. The differences attributed to the biomechanical model showed significantly greater (P ≤ .001) RMSE than the technological error. RMSE was systematically higher (P ≤ .001) for the long complex task with a mean on all joints of 2.8° compared to 1.2° during short functional movements. Definition of local coordinate systems based on anatomical landmarks or single posture was the most influent difference between the two systems. Additionally, IMUs accuracy was affected by the complexity and duration of the tasks. Nevertheless, technological error remained under 5° RMSE during handling tasks, which shows potential to track workers during their daily labour.

Wearable inertial sensors for human movement analysis

INTRODUCTION

The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.

AREAS COVERED

Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice. Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine.

Inverse Kinematics for Upper Limb Compound Movement Estimation in Exoskeleton-Assisted Rehabilitation

Robot-Assisted Rehabilitation (RAR) is relevant for treating patients affected by nervous system injuries (e.g., stroke and spinal cord injury). The accurate estimation of the joint angles of the patient limbs in RAR is critical to assess the patient improvement. The economical prevalent method to estimate the patient posture in Exoskeleton-based RAR is to approximate the limb joint angles with the ones of the Exoskeleton. This approximation is rough since their kinematic structures differ. Motion capture systems (MOCAPs) can improve the estimations, at the expenses of a considerable overload of the therapy setup. Alternatively, the Extended Inverse Kinematics Posture Estimation (EIKPE) computational method models the limb and Exoskeleton as differing parallel kinematic chains. EIKPE has been tested with single DOF movements of the wrist and elbow joints. This paper presents the assessment of EIKPE with elbow-shoulder compound movements (i.e., object prehension). Ground-truth for estimation assessment is obtained from an optical MOCAP (not intended for the treatment stage). The assessment shows EIKPE rendering a good numerical approximation of the actual posture during the compound movement execution, especially for the shoulder joint angles. This work opens the horizon for clinical studies with patient groups, Exoskeleton models, and movements types.

Ambulatory assessment of shoulder abduction strength curve using a single wearable inertial sensor

The aim of the present article was to assess the reliability of strength curves as determined from tridimensional linear accelerations and angular velocities measured by a single inertial measurement unit (IMU) fixed on the upper arm during a shoulder abduction movement performed holding a 1 kg dumbbell in the hand. Within-subject repeatability of the task was assessed on 45 subjects performing four trials consisting of one maximal shoulder abduction-adduction movement. Intraclass correlation coefficient (ICC) was computed on the average movement angular velocity (VEL) and range of movement (ROM) across the four trials. Within-subject repeatability of torque curves was assessed in terms of waveform similarities by computing the coefficient of multiple determination (CMD). Accuracy of the estimated ROM was assessed using an isokinetic dynamometer. High ICC values of ROM (0.955) and VEL (0.970) indicated a high within-subject repeatability of the task. A high waveform similarity of torque curves was also found between trials (CMD = 0.867). Accuracy with respect to isokinetic dynamometer in estimating ROM was always <1 degree (p = 0.37). This study showed the effectiveness of using a single wearable IMU for the assessment of strength curve during isoinertial movements in a way that complies with the needs of clinicians in an ambulatory setting.

Accuracy and repeatability of an inertial measurement unit system for field-based occupational studies

The accuracy and repeatability of an inertial measurement unit (IMU) system for directly measuring trunk angular displacement and upper arm elevation were evaluated over eight hours (i) in comparison to a gold standard, optical motion capture (OMC) system in a laboratory setting, and (ii) during a field-based assessment of dairy parlour work. Sample-to-sample root mean square differences between the IMU and OMC system ranged from 4.1° to 6.6° for the trunk and 7.2°-12.1° for the upper arm depending on the processing method. Estimates of mean angular displacement and angular displacement variation (difference between the 90th and 10th percentiles of angular displacement) were observed to change <4.5° on average in the laboratory and <1.5° on average in the field per eight hours of data collection. Results suggest the IMU system may serve as an acceptable instrument for directly measuring trunk and upper arm postures in field-based occupational exposure assessment studies with long sampling durations. Practitioner Summary: Few studies have evaluated inertial measurement unit (IMU) systems in the field or over long sampling durations. Results of this study indicate that the IMU system evaluated has reasonably good accuracy and repeatability for use in a field setting over a long sampling duration.

Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies

OBJECTIVES

The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies.

METHODS

We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function.

RESULTS

We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human-machine interfaces.

CONCLUSION

The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery.

SIGNIFICANCE

Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impairment.

Quadrupedal Locomotion-Respiration Entrainment and Metabolic Economy in Cross-Country Skiers

A 1:1 locomotion-respiration entrainment is observed in galloping quadrupeds, and is thought to improve running economy. However, this has not been tested directly in animals, as animals cannot voluntarily disrupt this entrainment. The purpose of this study was to evaluate metabolic economy in a human gait involving all four limbs, cross-country skiing, in natural entrainment and forced nonentrainment. Nine elite cross-country skiers roller skied at constant speed using the 2-skate technique. In the first and last conditions, athletes used the natural entrained breathing pattern: inhaling with arm recovery and exhaling with arm propulsion, and in the second condition, the athletes disentrained their breathing pattern. The rate of oxygen uptake (VO2) and metabolic rate (MR) were measured via expired gas analysis. Propulsive forces were measured with instrumented skis and poles. VO2 and MR increased by 4% and 5% respectively when skiers used the disentrained compared with the entrained breathing pattern. There were no differences in ski or pole forces or in timing of the gait cycle between conditions. We conclude that breathing entrainment reduces metabolic cost of cross-country skiing by approximately 4%. Further, this reduction is likely a result of the entrainment rather than alterations in gait mechanics.

Cognitive Learning, Monitoring and Assistance of Industrial Workflows Using Egocentric Sensor Networks

Today, the workflows that are involved in industrial assembly and production activities are becoming increasingly complex. To efficiently and safely perform these workflows is demanding on the workers, in particular when it comes to infrequent or repetitive tasks. This burden on the workers can be eased by introducing smart assistance systems. This article presents a scalable concept and an integrated system demonstrator designed for this purpose. The basic idea is to learn workflows from observing multiple expert operators and then transfer the learnt workflow models to novice users. Being entirely learning-based, the proposed system can be applied to various tasks and domains. The above idea has been realized in a prototype, which combines components pushing the state of the art of hardware and software designed with interoperability in mind. The emphasis of this article is on the algorithms developed for the prototype: 1) fusion of inertial and visual sensor information from an on-body sensor network (BSN) to robustly track the user's pose in magnetically polluted environments; 2) learning-based computer vision algorithms to map the workspace, localize the sensor with respect to the workspace and capture objects, even as they are carried; 3) domain-independent and robust workflow recovery and monitoring algorithms based on spatiotemporal pairwise relations deduced from object and user movement with respect to the scene; and 4) context-sensitive augmented reality (AR) user feedback using a head-mounted display (HMD). A distinguishing key feature of the developed algorithms is that they all operate solely on data from the on-body sensor network and that no external instrumentation is needed. The feasibility of the chosen approach for the complete action-perception-feedback loop is demonstrated on three increasingly complex datasets representing manual industrial tasks. These limited size datasets indicate and highlight the potential of the chosen technology as a combined entity as well as point out limitations of the system.

Detecting Elementary Arm Movements by Tracking Upper Limb Joint Angles With MARG Sensors

This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a two-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and four stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a seminaturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and seminaturalistic experiment, respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the seminaturalistic were detected correctly. Finally, the detection ratio remains close ( ±6%) to the average value, for different task durations further attesting to the algorithms robustness.

Lower Extremity Joint Angle Tracking with Wireless Ultrasonic Sensors during a Squat Exercise

This paper presents an unrestrained measurement system based on a wearable wireless ultrasonic sensor network to track the lower extremity joint and trunk kinematics during a squat exercise with only one ultrasonic sensor attached to the trunk. The system consists of an ultrasound transmitter (mobile) and multiple receivers (anchors) whose positions are known. The proposed system measures the horizontal and vertical displacement, together with known joint constraints, to estimate joint flexion/extension angles using an inverse kinematic model based on the damped least-squares technique. The performance of the proposed ultrasonic measurement system was validated against a camera-based tracking system on eight healthy subjects performing a planar squat exercise. Joint angles estimated from the ultrasonic system showed a root mean square error (RMSE) of 2.85° ± 0.57° with the reference system. Statistical analysis indicated great agreements between these two systems with a Pearson's correlation coefficient (PCC) value larger than 0.99 for all joint angles' estimation. These results show that the proposed ultrasonic measurement system is useful for applications, such as rehabilitation and sports.

Human Joint Angle Estimation with Inertial Sensors and Validation with A Robot Arm

Traditionally, human movement has been captured primarily by motion capture systems. These systems are costly, require fixed cameras in a controlled environment, and suffer from occlusion. Recently, the availability of low-cost wearable inertial sensors containing accelerometers, gyroscopes, and magnetometers have provided an alternative means to overcome the limitations of motion capture systems. Wearable inertial sensors can be used anywhere, cannot be occluded, and are low cost. Several groups have described algorithms for tracking human joint angles. We previously described a novel approach based on a kinematic arm model and the Unscented Kalman Filter (UKF). Our proposed method used a minimal sensor configuration with one sensor on each segment. This paper reports significant improvements in both the algorithm and the assessment. The new model incorporates gyroscope and accelerometer random drift models, imposes physical constraints on the range of motion for each joint, and uses zero-velocity updates to mitigate the effect of sensor drift. A high-precision industrial robot arm precisely quantifies the performance of the tracker during slow, normal, and fast movements over continuous 15-min recording durations. The agreement between the estimated angles from our algorithm and the high-precision robot arm reference was excellent. On average, the tracker attained an RMS angle error of about 3(°) for all six angles. The UKF performed slightly better than the more common Extended Kalman Filter.

Upper limb joint angle measurement in occupational health

Usual human motion capture systems are designed to work in controlled laboratory conditions. For occupational health, instruments that can measure during normal daily life are essential, as the evaluation of the workers' movements is a key factor to reduce employee injury- and illness-related costs. In this paper, we present a method for joint angle measurement, combining inertial sensors (accelerometers and gyroscopes) and magnetic sensors. This method estimates wrist flexion, wrist lateral deviation, elbow flexion, elbow pronation, shoulder flexion, shoulder abduction and shoulder internal rotation. The algorithms avoid numerical integration of the signals, which allows for long-time estimations without angle estimation drift. The system has been tested both under laboratory and field conditions. Controlled laboratory tests show mean estimation errors between 0.06° and of 1.05°, and standard deviation between 2.18° and 9.20°. Field tests seem to confirm these results when no ferromagnetic materials are close to the measurement system.

A wearable wireless ultrasonic sensor network for human arm motion tracking

This paper introduces a novel method for arm flexion/extension angles measurement using wireless ultrasonic sensor network. The approach uses unscented Kalman filter and D-H kinematical chain model to retrieve the joint angles. This method was experimentally validated by calculating the 2-dimensional wrist displacements from one mobile, placed on the point of subject's wrist, and four anchors. The performance of the proposed ultrasonic motion analysis system was bench-marked by commercial camera motion capture system. The experimental results demonstrate that a favorable performance of the proposed system in the estimation of upper limb motion. The proposed system is wireless, easy to wear, to use and much cheaper than current camera system. Thus, it has the potential to become a new and useful tool for routine clinical assessment of human motion.

A Comparison of Examination Equipment Used During Common Clinical Ophthalmologic Tasks

Background

Ophthalmologists report a high prevalence of work-related musculoskeletal symptoms, particularly of the neck and shoulders. Improving the design of equipment used in the clinical environment may reduce exposures to physical risk factors (e.g., sustained muscular exertions and non-neutral postures) associated with neck and shoulder pain among ophthalmologists.

Purpose

To compare estimates of neck and shoulder muscle activity and upper arm posture during use of conventional and alternative examination equipment common in clinical ophthalmologic practice.

Methods

Fifteen ophthalmologists performed one mock clinical examination using conventional equipment and one mock clinical examination using alternative equipment with the potential to reduce exposure to sustained muscular exertions and non-neutral upper arm postures. The alternative equipment included a slit-lamp biomicroscope with inclined viewing oculars, adjustable elbow supports, and a wider table-top with more room for supporting the arms in comparison to the conventional slit-lamp biomicroscope. A wireless binocular indirect ophthalmoscope was also evaluated that had a more even weight distribution than the conventional design. Measurements of upper trapezius and anterior deltoid muscle activity, upper arm posture, and perceived usability were used to compare the conventional and alternative equipment.

Results

In comparison to the conventional slit lamp biomicroscope, the alternative slit lamp biomicroscope led to (i) 12% to 13% reductions in upper trapezius muscle activity levels, (ii) a 9% reduction in left anterior deltoid muscle activity levels, and (iii) a 15% reduction in the percentage of work time spent with the left upper arm elevated in positions greater than 60°. In addition, participants rated the comfort and adjustability of both the alternative slit lamp biomicroscope and binocular indirect ophthalmoscope more favorably than the conventional equipment.

Conclusions

The results suggest that the alternative slit-lamp biomicroscope may help to reduce overall muscular demands and non-neutral postures of the neck and shoulder region among ophthalmologists.

Miniature low-power inertial sensors: promising technology for implantable motion capture systems

Inertial and magnetic sensors are valuable for untethered, self-contained human movement analysis. Very recently, complete integration of inertial sensors, magnetic sensors, and processing into single packages, has resulted in miniature, low power devices that could feasibly be employed in an implantable motion capture system. We developed a wearable sensor system based on a commercially available system-in-package inertial and magnetic sensor. We characterized the accuracy of the system in measuring 3-D orientation-with and without magnetometer-based heading compensation-relative to a research grade optical motion capture system. The root mean square error was less than 4° in dynamic and static conditions about all axes. Using four sensors, recording from seven degrees-of-freedom of the upper limb (shoulder, elbow, wrist) was demonstrated in one subject during reaching motions. Very high correlation and low error was found across all joints relative to the optical motion capture system. Findings were similar to previous publications using inertial sensors, but at a fraction of the power consumption and size of the sensors. Such ultra-small, low power sensors provide exciting new avenues for movement monitoring for various movement disorders, movement-based command interfaces for assistive devices, and implementation of kinematic feedback systems for assistive interventions like functional electrical stimulation.

Continuous monitoring of turning in patients with movement disability

Difficulty with turning is a major contributor to mobility disability and falls in people with movement disorders, such as Parkinson's disease (PD). Turning often results in freezing and/or falling in patients with PD. However, asking a patient to execute a turn in the clinic often does not reveal their impairments. Continuous monitoring of turning with wearable sensors during spontaneous daily activities may help clinicians and patients determine who is at risk of falls and could benefit from preventative interventions. In this study, we show that continuous monitoring of natural turning with wearable sensors during daily activities inside and outside the home is feasible for people with PD and elderly people. We developed an algorithm to detect and characterize turns during gait, using wearable inertial sensors. First, we validate the turning algorithm in the laboratory against a Motion Analysis system and against a video analysis of 21 PD patients and 19 control (CT) subjects wearing an inertial sensor on the pelvis. Compared to Motion Analysis and video, the algorithm maintained a sensitivity of 0.90 and 0.76 and a specificity of 0.75 and 0.65, respectively. Second, we apply the turning algorithm to data collected in the home from 12 PD and 18 CT subjects. The algorithm successfully detects turn characteristics, and the results show that, compared to controls, PD subjects tend to take shorter turns with smaller turn angles and more steps. Furthermore, PD subjects show more variability in all turn metrics throughout the day and the week.

A multifunctional joint angle sensor with measurement adaptability

The paper presents a multifunctional joint sensor with measurement adaptability for biological engineering applications, such as gait analysis, gesture recognition, etc. The adaptability is embodied in both static and dynamic environment measurements, both of body pose and in motion capture. Its multifunctional capabilities lay in its ability of simultaneous measurement of multiple degrees of freedom (MDOF) with a single sensor to reduce system complexity. The basic working mode enables 2DOF spatial angle measurement over big ranges and stands out for its applications on different joints of different individuals without recalibration. The optional advanced working mode enables an additional DOF measurement for various applications. By employing corrugated tube as the main body, the sensor is also characterized as flexible and wearable with less restraints. MDOF variations are converted to linear displacements of the sensing elements. The simple reconstruction algorithm and small outputs volume are capable of providing real-time angles and long-term monitoring. The performance assessment of the built prototype is promising enough to indicate the feasibility of the sensor.

Body sensor network-based strapdown orientation estimation: application to human locomotion

In this contribution, inertial and magnetic sensors are considered for real-time strapdown orientation tracking of human limb or robotic segment orientation. By using body sensor network integrated triaxial gyrometer, accelerometer, and magnetometer measurements, two orientation estimation filters are presented and subsequently designed for bias insensitive tracking of human gait. Both filters use quaternions for rotation representation, where preprocessing accelerometer and magnetometer data is conducted with the quaternion based estimation algorithm (QUEST) as a reference filter input. This results in a significant reduction of the complexity and calculation cost on the body sensor network. QUEST-based preprocessed attitude data is used for the designed extended Kalman filter (EKF) and a new complementary sliding mode observer. EKF-QUEST and complementary sliding mode observer are designed and tested in simulations and subsequently validated with a reference motion tracking system in treadmill tests.

Lower limb spasticity assessment using an inertial sensor: a reliability study

Spasticity is a common motor impairment in patients with neurological disorders that can prevent functional recovery after rehabilitation. In the clinical setting, its assessment is carried out using standardized clinical scales. The aim of this study was to verify the applicability of inertial sensors for an objective measurement of quadriceps spasticity and evaluate its test-retest and inter-rater reliability during the implementation of the Wartenberg pendulum test. Ten healthy subjects and 11 patients in vegetative state with severe brain damage were enrolled in this study. Subjects were evaluated three times on three consecutive days. The test-retest reliability of measurement was assessed in the first two days. The third day was devoted to inter-rater reliability assessment. In addition, the lower limb muscle tone was bilaterally evaluated at the knee joint by the modified Ashworth scale. The factorial ANOVA analysis showed that the implemented method allowed us to discriminate between healthy and pathological conditions. The fairly low SEM and high ICC values obtained for the pendulum parameters indicated a good test-retest and inter-rater reliability of measurement. This study shows that an inertial sensor can be reliably used to characterize leg kinematics during the Wartenberg pendulum test and provide quantitative evaluation of quadriceps spasticity.

A wearable inertial system to assess the cervical spine mobility: comparison with an optoelectronic-based motion capture evaluation

In clinical settings, the cervical range of motion (ROM) is commonly used to assess cervical spine function. This study aimed at assessing cervical spine mobility based on head and thorax kinematics measured with a wearable inertial system (WS). Sequences of imposed active head movements (lateral bending, axial rotation and flexion-extension) were recorded in ten controls and 13 patients who had undergone an arthrodesis. Orientation of the head relative to the thorax was computed in terms of 3D helical angles and compared with the values obtained using an optoelectronic reference system (RS). Movement patterns from WS and RS showed excellent concurrent validity (CMC up to 1.00), but presented slight differences of bias (mean bias<2.5°) and dispersion (mean dispersion<4.2°). ROM obtained using WS also showed some differences compared to RS (mean difference<5.7°), within the range of those reported in literature. WS enabled the observation of the same significant differences between controls and patients as RS. Moreover, ROM from WS presented good test-retest repeatability (ICC between 0.63 and 0.99 and SEM<6.2°). In conclusion, WS can provide angles and ROM comparable to those obtained with RS and relevant for the cervical assessment after treatment.