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Improved upper limb rehabilitation requires careful and re-constructed information around stroke patients' muscle activation characteristics and kinematic features in functional movement. Body Sensor Networks (BSN) are deployed to provide an immersive engagement of the rehabilitation exercise and translation into an augmented reality world for a higher order of analytics and consultation by medical consultants. Results of the analysis generate contextual intelligence to improve therapy programmes in order of an increased magnitude with derived information on model schemas, pattern deviation and effectiveness of diagnostics.

The behaviour of individual ball to track contact areas is studied in close conformity oil lubricated conditions. Although primarily concerned with aero-engine operating conditions the results are expressed in dimension-less forms which permit use over a wide field of ball race geometries. An arbitrary boundary is established, based upon electrical conductance through the oil film, between running conditions which produce minimal wear damage and conditions which are probably unacceptable from this point of view. The major purpose of the paper is to focus attention on this criterion and to relate ball to track kinematic situations to it.

Lower limb joint kinematics have been measured in laboratory settings using fixed camera-based motion capture systems; however, recently inertial measurement units (IMUs) have been developed as an alternative. The purpose of this study was to test a quaternion conversion (QC) method for calculating the three orthogonal knee angles during the high velocities associated with a jump landing using commercially available IMUs. Nine cadaveric knee specimens were instrumented with APDM Opal IMUs to measure knee kinematics in one-legged 3–4× bodyweight simulated jump landings, four of which were used in establishing the parameters (training) for the new method and five for validation (testing). We compared the angles obtained from the QC method to those obtained from a commercially available sensor and algorithm (APDM Opal) with those calculated from an active marker motion capture system. Results showed a significant difference between both IMU methods and the motion capture data in the majority of orthogonal angles (p

The purpose of this work is to investigate the efficiency of wearable assistive devices under different load-carriage walking. We designed an experimental platform with a lightweight ankle-assisted robot. Eight subjects were tested in three experimental conditions: free walk with load (FWL), power-off with load (POFL), and power-on with load for different levels of force at a walking speed of 3.6 km/h. We recorded the metabolic expenditure and kinematics of the subjects under three levels of load-carried (10%, 20%, and 30% of body mass). We define the critical force, where at a certain load, the robot inputs a certain force to the human body, and with the assistance of this force, the positive effect of the robot on the human body exactly compensates for the negative effect. The critical forces from the fit of the assistive force and metabolic cost curves were 130 N, 160 N, and 215 N at three different load levels. The intrinsic weight of our device increases mechanical work at the ankle as the load weight rises with 2.08 J, 2.43 J, and 2.73 J for one leg during a gait cycle. With weight bearing increasing, the ratio of the mechanical work input by the robot to the mechanical work output by the weight of the device decreases (from 0.904 to 0.717 and 0.513), verifying that the walking assistance efficiency of such devices decreases as the weight rises.

Human body need the soft characteristics of the endoscope, so traditional endoscope which has rigid feature is bound to be eliminated. Silicone, as the endoscope material, is the most suitable for the human colon. The pneumatic drive robot can achieve the flexible and fast purpose. However, the pneumatic drive robot is difficult to control and its control accuracy is low. Mechanical drive robot has high accuracy and easy control. So combination with the advantages of the pneumatic drive robot and the mechanical drive robot is very essential. A medical pneumatic robot is designed and kinematics analysis is proposed in this paper based on the analysis of the anatomical characteristics of human colon and the bionic principle of continuous robot. The medical pneumatic soft robot is driven by gas, which voids the shortcomings of the traditional rigid endoscopic. The traditional rigid endoscopic can’t adapt to the human complex colon and has invasive examination to the human body. In addition, this paper proposes a new method that uses mark string holes tube to inflate and deflate the pneumatic channel on the tube and uses the mechanical control outside the human body to realize driving the pneumatic bending system in the human body. Medical pneumatic soft robot is a new therapeutic system for minimally invasive surgery, serving as a new therapeutic method for lesions in organ chambers.

This paper aims to use bond graph modeling to create the most comprehensive finger tendon model and simulation to date. Current models are limited to either free motion without external contact or fixed finger force transmission between tendons and fingertip. The forward dynamics model, presented in this work, simultaneously simulates the kinematics of tendon-finger motion and contact forces of a central finger given finger tendon inputs. The model equations derived from bond graphs are accompanied by nonlinear relationships modeling the anatomical complexities of moment arms, tendon slacking, and joint range of motion (ROM). The structure of the model is validated using a robotic testbed, Utah's Anatomically correct Robotic Testbed (UART) finger. Experimental motion of the UART finger during free motion (no external contact) and surface contact are simulated using the bond graph model. The contact forces during the surface contact experiments are also simulated. On average, the model was able to predict the steady-state pose of the finger with joint angle errors less than 6 deg across both free motion and surface contact experiments. The static contact forces were accurately predicted with an average of 11.5% force magnitude error and average direction error of 12 deg.

Purpose The purpose of this study was to compare 4 techniques for arrival at a base after sprinting maximally to reach it: sliding head-first, sliding feet-first, running through the base without slowing, and stopping on the base. A secondary purpose of the study was to determine any advantage there may be to diving into first base to arrive sooner than running through the base. Methods Two high-definition video cameras were used to capture 3-dimensional kinematics of sliding techniques of 9 intercollegiate baseball players. Another video camera was used to time runs from first base to second in 4 counterbalanced conditions: running through the base, sliding head-first, sliding feet-first, and running to a stop. Mathematical modeling was used to simulate diving to first base such that the slide would begin when the hand touches the base. Results Based upon overall results, the quickest way to the base is by running through it, followed by head-first, feet-first, and running to a stop. Conclusion There was a non-significant trend toward an advantage for diving into first base over running through it, but more research is needed, and even if the advantage is real, the risks of executing this technique probably outweigh the miniscule gain.

Background: Naturalistic driving studies, designed to objectively assess driving behavior and outcomes, are conducted by equipping vehicles with dedicated instrumentation (eg, accelerometers, gyroscopes, Global Positioning System, and cameras) that provide continuous recording of acceleration, location, videos, and still images for eventual retrieval and analyses. However, this research is limited by several factors: the cost of equipment installation; management and storage of the large amounts of data collected; and data reduction, coding, and analyses. Modern smartphone technology includes accelerometers built into phones, and the vast, global proliferation of smartphones could provide a possible low-cost alternative for assessing kinematic risky driving. Objective: We evaluated an in-house developed iPhone app (gForce) for detecting elevated g-force events by comparing the iPhone linear acceleration measurements with corresponding acceleration measurements obtained with both a custom Android app and the in-vehicle miniDAS data acquisition system (DAS; Virginia Tech Transportation Institute). Methods: The iPhone and Android devices were dashboard-mounted in a vehicle equipped with the DAS instrumentation. The experimental protocol consisted of driving maneuvers on a test track, such as cornering, braking, and turning that were performed at different acceleration levels (ie, mild, moderate, or hard). The iPhone gForce app recorded linear acceleration (ie, gravity-corrected). The Android app recorded gravity-corrected and uncorrected acceleration measurements, and the DAS device recorded gravity-uncorrected acceleration measurements. Lateral and longitudinal acceleration measures were compared. Results: The correlation coefficients between the iPhone and DAS acceleration measurements were slightly lower compared to the correlation coefficients between the Android and DAS, possibly due to the gravity correction on the iPhone. Averaging the correlation coefficients for all maneuvers, the longitudinal and lateral acceleration measurements between iPhone and DAS were rlng=0.71 and rlat=0.83, respectively, while the corresponding acceleration measurements between Android and DAS were rlng=0.95 and rlat=0.97. The correlation coefficients between lateral accelerations on all three devices were higher than with the corresponding longitudinal accelerations for most maneuvers. Conclusions: The gForce iPhone app reliably assessed elevated g-force events compared to the DAS. Collectively, the gForce app and iPhone platform have the potential to serve as feature-rich, inexpensive, scalable, and open-source tool for assessment of kinematic risky driving events, with potential for research and feedback forms of intervention. [JMIR Mhealth Uhealth 2018;6(4):e69]

The triple jump is a demanding athletics event that, after an approach run, consists of three consecutive phases: the hop, the bound, and the jump. During the involved three take-off actions a jumper is exposed to increased risk of injury due to the high impact forces from the ground and powerful muscle/tendon efforts, which are further reflected in the internal loads of the lower limb joints. While external ground reactions can possibly be measured using force platforms, in vivo measurements of the internal loads are practically not feasible. The purpose of the paper is to present the development of an effective formulation for the inverse dynamics simulation of the triple jump, based on the jumper dynamical model and non-invasive kinematic recordings of the movement. The developed simulation model serves for the analysis of all the triple jump phases, irrespective of whether the jumper is in flight or in contact with the ground with one of his feet, and is focused on effective assessment of the external reactions on the supporting leg as well as the muscle forces and joint reaction forces in the leg. Some numerical results of inverse dynamics simulation of the triple jump are reported.

This paper based on the improved twin tripod sliding universal joint and let it has the determined movement output. And create the three-dimensional model of twin tripod sliding universal joint before and after improved in software pro/e, and then put it into the simulation module, carry out the kinematic simulation of the tripod center on coupling. Obtained the curves of displacement, velocity and acceleration for two kinds of tripod centers.

Standing balanced reach is a fundamental task involved in many activities of daily living that has not been well analyzed quantitatively to assess and characterize the multisegmental nature of the body's movements. We developed a dynamic balanced reach test (BRT) to analyze performance in this activity; in which a standing subject is required to maintain balance while reaching and pointing to a target disk moving across a large projection screen according to a sum-of-sines function. This tracking and balance task is made progressively more difficult by increasing the disk's overall excursion amplitude. Using kinematic and ground reaction force data from 32 young healthy subjects, we investigated how the motions of the tracking finger and whole-body center of mass (CoM) varied in response to the motion of the disk across five overall disk excursion amplitudes. Group representative performance statistics for the cohort revealed a monotonically increasing root mean squared (RMS) tracking error (RMSE) and RMS deviation (RMSD) between whole-body CoM (projected onto the ground plane) and the center of the base of support (BoS) with increasing amplitude (p

The knee joint is at the center of a kinetic chain that includes the foot, ankle joint, lower and upper leg, hip joint, and pelvis. The complexity of the knee, the fact that it bears most of the body's weight, and its centrality to human movement make the knee vulnerable to acute and chronic injuries. Contact loading is a contributing factor in the onset and progression of osteoarthritis [1], and it is equally important in artificial knee replacement wear [2,3], which leads to failure of total knee prostheses. Knowledge of in vivo joint loads and motions during activities of daily living is also crucial in tissue engineering, injury prevention, and in developing, evaluating, and optimizing therapeutic strategies. Instrumented knee prostheses capable of measuring joint loading during dynamic activities have been implanted in patients [4,5–7]. But implementing these devices is expensive, invasive, and the results may not necessarily transfer to other patients and healthy adults. Thus, in vivo joint loads must be predicted using computational models. Estimation of the in vivo knee loads under dynamic conditions is highly dependent on correct prediction of muscle forces. This requires resolution of the muscle redundancy problem, which occurs because the muscle force solutions to create a certain motion are not unique. A number of studies addressed the muscle redundancy problem by using optimization methods to converge to a set of muscle forces that recreate the kinematics [8–14]. This approach involves two stages. In the first stage the muscle and net joint torques are estimated by inverse dynamics (static optimization) or by forward dynamic optimization. Static optimization uses the ground reaction forces as an input and solves the dynamics equations at each time frame. Muscle forces are predicted at each time step such that the kinematics are consistent with the experimentally measured motions. In dynamic optimization, the dynamics equations are numerically integrated across all time frames; thus, the muscle forces and kinematics are predicted concurrently. Typically, articular contact models are not included in the first stage, and the computed forces are applied to an articular contact model of the knee to estimate the joint contact forces. There are several limitations in this approach. Because articular contacts are not included in the first stage and the knee is often modeled as a one degree-of-freedom idealized joint, the contact forces do not affect the predicted muscle forces. Implicitly, the assumption is that muscle forces do not contribute to motions outside of knee flexion-extension. In a study by Lin et al. [14], it was demonstrated that muscle forces are affected by the number of constraints included in the knee model. In the case of static optimization, the accuracy of the available motion and ground reaction force data influences the muscle force predictions [9]. Moreover, since the ground reaction forces are used as an input, they cannot be used to evaluate the performance of the model. Finally, optimization methods assume that human movement is bound by minimizing some cost function such as sum of squares of muscle activation or metabolic energy, but it is unknown if the nervous system produces motion in such a way. Another major challenge in estimating the in vivo joint loads is the correct resolution of the tibiofemoral contact force into the medial and lateral compartment contact forces. The two regions of contact between the femur and the tibia create a contact force redundancy issue, as well [4]. Most musculoskeletal modeling studies tend to overestimate the tibial contact forces during gait [2,15–18] when compared to in vivo measurements collected by instrumented implants [5–7,19]. The discrepancies between model predictions and measured joint loads are even greater when the medial to lateral compartment load distributions are considered. The distribution of the tibiofemoral contact forces depends on the magnitude of the moments acting about the knee joint and the individual contributions of the muscles and ligaments [15,18]. In this paper, we present a two stage modeling method to predict tibiofemoral contact loads during treadmill walking. Our work is distinguished from previous published studies in a number of ways. In the first stage of our method, kinematic data is the only input into the multibody model while the knee joint (including both the tibiofemoral and patellofemoral joints) is allowed 12 degrees-of-freedom. The tibiofemoral and patellofemoral joints are constrained by contact between articulating surfaces as well as ligament forces. In the second stage, muscle forces and joint torques are predicted by using a proportional integral derivative (PID) controller, and the contact forces are predicted concurrently. Moreover, the ground reaction forces, as well as electromyography (EMG) and segment kinematics are used to validate the performance of the forward simulation. Because the ground reaction forces are never used as an input to the model, a detailed model of the foot-shoe-floor is created. Our specific aims are (a) to develop a full body, musculoskeletal model with subject specific lower extremity geometries that includes a detailed model of the foot/floor interactions that can concurrently predict muscle, ligament, and contact forces and (b) to evaluate the tibiofemoral joint load predictions with measured tibial loads for two different cycles of treadmill gait.

Background: Postural instability is one of the major complications found in people who survive a stroke. Parameterizing the Functional Reach Test (FRT) could be useful in clinical practice and basic research, as this test is a clinically accepted tool (for its simplicity, reliability, economy, and portability) to measure the semistatic balance of a subject. Objective: The aim of this study is to analyze the reliability in the FRT parameterization using inertial sensor within mobile phones (mobile sensors) for recording kinematic variables in patients who have suffered a stroke. Our hypothesis is that the sensors in mobile phones will be reliable instruments for kinematic study of the FRT. Methods: This is a cross-sectional study of 7 subjects over 65 years of age who suffered a stroke. During the execution of FRT, the subjects carried two mobile phones: one placed in the lumbar region and the other one on the trunk. After analyzing the data obtained in the kinematic registration by the mobile sensors, a number of direct and indirect variables were obtained. The variables extracted directly from FRT through the mobile sensors were distance, maximum angular lumbosacral/thoracic displacement, time for maximum angular lumbosacral/thoracic displacement, time of return to the initial position, and total time. Using these data, we calculated speed and acceleration of each. A descriptive analysis of all kinematic outcomes recorded by the two mobile sensors (trunk and lumbar) was developed and the average range achieved in the FRT. Reliability measures were calculated by analyzing the internal consistency of the measures with 95% confidence interval of each outcome variable. We calculated the reliability of mobile sensors in the measurement of the kinematic variables during the execution of the FRT. Results: The values in the FRT obtained in this study (2.49 cm, SD 13.15) are similar to those found in other studies with this population and with the same age range. Intrasubject reliability values observed in the use of mobile phones are all located above 0.831, ranging from 0.831 (time B_C trunk area) and 0.894 (displacement A_B trunk area). Likewise, the observed intersubject values range from 0.835 (time B_C trunk area) and 0.882 (displacement A_C trunk area). On the other hand, the reliability of the FRT was 0.989 (0.981-0.996) and 0.978 (0.970-0.985), intrasubject and intersubject respectively. Conclusions: We found that mobile sensors in mobile phones could be reliable tools in the parameterization of the Functional Reach Test in people who have had a stroke. [JMIR Rehabil Assist Technol 2015;2(1):e6]

Background: In recent years, there has been a great interest in analyzing upper-limb kinematics. Inertial measurement with mobile phones is a convenient and portable analysis method for studying humerus kinematics in terms of angular mobility and linear acceleration. Objective: The aim of this analysis was to study upper-limb kinematics via mobile phones through six physical properties that correspond to angular mobility and acceleration in the three axes of space. Methods: This cross-sectional study recruited healthy young adult subjects. Humerus kinematics was studied in 10 young adults with the iPhone4. They performed flexion and abduction analytical tasks. Mobility angle and lineal acceleration in each of its axes (yaw, pitch, and roll) were obtained with the iPhone4. This device was placed on the right half of the body of each subject, in the middle third of the humerus, slightly posterior. Descriptive statistics were calculated. Results: Descriptive graphics of analytical tasks performed were obtained. The biggest range of motion was found in pitch angle, and the biggest acceleration was found in the y-axis in both analytical tasks. Focusing on tridimensional kinematics, bigger range of motion and acceleration was found in abduction (209.69 degrees and 23.31 degrees per second respectively). Also, very strong correlation was found between angular mobility and linear acceleration in abduction (r=.845) and flexion (r=.860). Conclusions: The use of an iPhone for humerus tridimensional kinematics is feasible. This supports use of the mobile phone as a device to analyze upper-limb kinematics and to facilitate the evaluation of the patient.

Much of the hand's functional capacity is due to the versatility of the motions at the thumb carpometacarpal (CMC) joint, which are presently incompletely defined. The aim of this study was to develop a mathematical model to completely describe the envelope of physiological motion of the thumb CMC joint and then to examine if there were differences in the kinematic envelope between women and men. In vivo kinematics of the first metacarpal with respect to the trapezium were computed from computed tomography (CT) volume images of 44 subjects (20M, 24F, 40.3 ± 17.7 yr) with no signs of CMC joint pathology. Kinematics of the first metacarpal were described with respect to the trapezium using helical axis of motion (HAM) variables and then modeled with discrete Fourier analysis. Each HAM variable was fit in a cyclic domain as a function of screw axis orientation in the trapezial articular plane; the RMSE of the fits was 14.5 deg, 1.4 mm, and 0.8 mm for the elevation, location, and translation, respectively. After normalizing for the larger bone size in men, no differences in the kinematic variables between sexes could be identified. Analysis of the kinematic data also revealed notable coupling of the primary rotations of the thumb with translation and internal and external rotations. This study advances our basic understanding of thumb CMC joint function and provides a complete description of the CMC joint for incorporation into future models of hand function. From a clinical perspective, our findings provide a basis for evaluating CMC pathology, especially the mechanically mediated aspects of osteoarthritis (OA), and should be used to inform artificial joint design, where accurate replication of kinematics is essential for long-term success.

Accessing a specific, predefined location identified in medical images is a common interventional task for biopsies and drug or therapy delivery. While conventional surgical needles provide little steerability, concentric tube continuum devices enable steering through curved trajectories. These devices are usually developed as robotic systems. However, manual actuation of concentric tube devices is particularly useful for initial transfer into the clinic since the Food and Drug Administration (FDA) and Institutional Review Board (IRB) approval process of manually operated devices is simple compared to their motorized counterparts. In this paper, we present a manual actuation device for the deployment of steerable cannulas. The design focuses on compactness, modularity, usability, and sterilizability. Further, the kinematic mapping from joint space to Cartesian space is detailed for an example concentric tube device. Assessment of the device’s accuracy was performed in free space, as well as in an image-guided surgery setting, using tracked 2D ultrasound.

Detailed knowledge of joint kinematics and loading is essential for improving the design and surgical outcomes of total knee replacement surgeries and tissue engineering applications. Instrumented prosthetics that are capable of measuring joint loading during ambulatory activities have been implanted in patients [1–3], but implementation of these devices is expensive and the number of patients using instrumented prosthetics is limited. Experimentally measured joint loading is often augmented with traditional gait laboratory measurements including motion capture, ground reaction forces, and muscle activations through electromyography (EMG). Computational models can enhance these experimental measurements by providing detailed information on joint contact mechanics and kinematics in addition to loading. Dynamic loading is a contributing factor in the progression of joint osteoarthritis [4] and is equally important in artificial knee replacement wear [5,6]. A dynamic computational model in which muscle, ligament, and articular surface contact forces are predicted concurrently would be the ideal tool for improving implant design and objective planning of surgical treatments. The most important hurdle to using computational models in the clinic is the validation of the estimated in vivo contact, ligament, and muscle forces. The majority of published three-dimensional multibody simulations that included a knee contact model are quasi-static [7–13], which prevents the prediction of muscle and ligament forces alongside joint contact pressures during dynamic conditions. Over a decade ago, Piazza and Delp [14] produced a forward-dynamic simulation of a step-up task that combined forces from 13 EMG driven muscles crossing a prosthetic knee, forces from collateral ligaments modeled as nonlinear elastic springs, and forces from rigid contacts defined between tibio-femoral and patella-femoral prosthetic component geometries. Since publication of the Piazza and Delp paper in 2001, dynamic models that combine muscle forces, ligament forces, and contact forces from knee geometries have been rare. Although the need for concurrent dynamic models that link motion, muscle forces, and joint contacts has been recognized [14–16], a body-level forward-dynamic movement simulation that combines muscles, ligaments, and contact mechanics of the knee geometries does not exist in recent literature. Several models using net joint loading predicted from body-level inverse dynamic simulations combined with static optimization models at the knee level have been developed to predict tibia contact forces. These models represent the knee as a hinge joint at the body level, but the resulting joint load predictions have generally agreed well with experimental measurements. For example, Kim et al. compared model predicted tibia contact forces to values from an instrumented prosthetic [17]. In this modeling scheme, net joint moments from an inverse dynamics simulation were used to predict muscle forces from static optimization (minimizing of the sum of squares of muscle activations). The muscle forces along with ground reaction forces and prosthetic component motion from fluoroscopy were fed into a subject specific multibody knee model. Deformable contacts in the knee model then predicted the tibia contact forces. Recently, Lundberg et al. compared predicted knee loading to experimental knee loading from an instrumented prosthetic from four subjects [18]. A parametric model was used to find a contact solution space for parametrically varied muscle activations by solving the static equilibrium equations at discrete time steps in the gait cycle. Net knee loading from traditional inverse dynamics gait analysis provided the external moments and forces [19]. The finite element method has been widely used to model the relationship between kinematics and contact mechanics in prosthetic knee replacements. Static forces at discrete flexion angles have been applied to finite element models to determine component stress [6]. In addition prosthetic wear simulators [20] and experimental joint simulators [16,21,22] have been used to provide dynamic joint boundaries and loading. Zelle et al. recently simulated a weight-bearing squatting motion by applying ground reaction forces to the distal tibia and incrementally releasing a constrained quadriceps tendon to achieve knee flexion [23]. Explicit dynamic analysis is typically used for finite element models that include dynamic loading conditions. Halloran et al. found that rigid-body analyses produced kinematics that were nearly identical to that of a deformable finite element model of a prosthetic knee loaded in a dynamic knee simulator [16]. In addition, the rigid-body analyses predicted contact pressures and area close to that of the deformable model at a fraction of the computational cost. Good agreement with experimental contact pressures has also been demonstrated in rigid body prosthetic models using elastic foundation theory to represent contact between femoral and tibial components [24]. Body-level forward-dynamic movement simulations with concurrent predictions of muscle, ligament, and contact forces that are compared to in vivo measurements of knee loading do not exist in the literature. The data provided by the “Grand Challenge Competition to Predict In-Vivo Knee Loads” for the 2012 American Society of Mechanical Engineers Summer Bioengineering Conference [1] provided an opportunity to create and validate such a model. The objective of the present study was to develop a full body, muscle driven, dynamic, subject specific model during squat and toe-rise motions in the multibody framework capable of concurrent predictions of muscle and contact forces as well as detailed estimation of the contact area patches on the tibial insert. The squat and toe-rise motions were chosen for this study before attempting the more complicated gait trials. The model combined anatomically correct geometries for the lower extremities with anthropometric and motion data from a female subject with an instrumented knee implant. The model knee force predictions were compared against the in vivo measurements of joint contact loading acquired from the patient. Moreover, the muscle activation patterns and ground reaction forces were compared to experimental data and the kinematic accuracy of the muscle driven simulation was evaluated. Detailed contact force and contact area predictions are achieved by discretizing the implanted tibia tray into multiple hexahedral elements.

Prior to developing any specific fall detection algorithm, it is critical to distinguish the unique motion features associated with fall accidents. The current study aimed to investigate the upper trunk angular kinematics during slip-induced backward falls and activities of daily living (ADLs). Ten healthy older adults (age = 75 ± 6 yr (mean ± SD)) were involved in a laboratory study. Sagittal trunk angular kinematics were measured using optical motion analysis system during normal walking, slip-induced backward falls, lying down, bending over, and various types of sitting down (SN). Trunk angular phase-plane plots were generated to reveal the motion features of falls. It was found that backward falls were characterized by a simultaneous occurrence of a slight trunk extension and an extremely high trunk extension velocity (peak average = 139.7 deg/s), as compared to ADLs (peak average = 84.1 deg/s). It was concluded that the trunk extension angular kinematics of falls were clearly distinguishable from those of ADLs from the perspective of angular phase-plane plot. Such motion features can be utilized in future studies to develop a new prior-to-impact fall detection algorithm.

This study presents a methodology to determine thumb and index finger kinematics while utilizing a minimal set of markers. The motion capture of skin-surface markers presents inherent challenges for the accurate and comprehensive measurement of digit kinematics. As such, it is desirable to utilize robust methods for assessing digit kinematics with fewer markers. The approach presented in this study involved coordinate system alignment, locating joint centers of rotation, and a solution model to estimate three-dimensional (3-D) digit kinematics. The solution model for each digit was based on assumptions of rigid-body interactions, specific degrees of freedom (DOFs) at each located joint, and the aligned coordinate system definitions. Techniques of inverse kinematics and optimization were applied to calculate the 3-D position and orientation of digit segments during pinching between the thumb and index finger. The 3-D joint center locations were reliably fitted with mean coefficients of variation below 5%. A parameterized form of the solution model yielded feasible solutions that met specified tolerance and convergence criteria for over 85% of the test points. The solution results were intuitive to the pinching function. The thumb was measured to be rotated about the CMC joint to bring it into opposition to the index finger and larger rotational excursions (>10 deg) were observed in flexion/extension compared to abduction/adduction and axial rotation for all joints. While the solution model produced results similar to those computed from a full marker set, the model facilitated the usage of fewer markers, which inherently lessened the effects of passive motion error and reduced the post-experimental effort required for marker processing.

The purpose of this study was to perform a blinded comparison of model predictions of total knee replacement contact forces to in vivo forces from an instrumented prosthesis during normal walking and medial thrust gait by participating in the “Third Grand Challenge Competition to Predict in vivo Knee Loads.” We also evaluated model assumptions that were critical for accurate force predictions. Medial, lateral, and total axial forces through the knee were calculated using a previously developed and validated parametric numerical model. The model uses equilibrium equations between internal and external moments and forces to obtain knee joint contact forces and calculates a range of forces at instances during the gait cycle through parametric variation of muscle activity levels. For 100 instances during a normal over-ground gait cycle, model root mean square differences from eTibia data were 292, 248, and 281 for medial, lateral, and total contact forces, respectively. For 100 instances during a medial thrust gait cycle, model root mean square differences from eTibia data were 332, 234, and 470 for medial, lateral, and total contact forces, respectively. The percent difference between measured and predicted peak total axial force was 2.89% at the first peak and 9.36% at the second peak contact force for normal walking and 3.94% at the first peak and 14.86% at the second peak contact force for medial thrust gait. After unblinding, changes to model assumptions improved medial and lateral force predictions for both gait styles but did not improve total force predictions. Axial forces computed with the model compared well to the eTibia data under blinded and unblinded conditions. Knowledge of detailed knee kinematics, namely anterior-posterior translation, appears to be critical in obtaining accurate force predictions.

We report an image segmentation and registration method for studying joint morphology and kinematics from in vivo magnetic resonance imaging (MRI) scans and its application to the analysis of foot and ankle joint motion. Using an MRI-compatible positioning device, a foot was scanned in a single neutral and seven other positions ranging from maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation. A segmentation method combining graph cuts and level set was developed. In the subsequent registration step, a separate rigid body transformation for each bone was obtained by registering the neutral position dataset to each of the other ones, which produced an accurate description of the motion between them. The segmentation algorithm allowed a user to interactively delineate 14 foot bones in the neutral position volume in less than 30 min total (user and computer processing unit [CPU]) time. Registration to the seven other positions took approximately 10 additional minutes of user time and 5.25 h of CPU time. For validation, our results were compared with those obtained from 3DViewnix, a semiautomatic segmentation program. We achieved excellent agreement, with volume overlap ratios greater than 88% for all bones excluding the intermediate cuneiform and the lesser metatarsals. For the registration of the neutral scan to the seven other positions, the average overlap ratio is 94.25%, while the minimum overlap ratio is 89.49% for the tibia between the neutral position and position 1, which might be due to different fields of view (FOV). To process a single foot in eight positions, our tool requires only minimal user interaction time (less than 30 min total), a level of improvement that has the potential to make joint motion analysis from MRI practical in research and clinical applications.

Background: Vertical jump tests are used in athletics and rehabilitation to measure physical performance in people of different age ranges and fitness. Jumping ability can be analyzed through different variables, and the most commonly used are fly time and jump height. They can be obtained by a variety of measuring devices, but most are limited to laboratory use only. The current generation of smartphones contains inertial sensors that are able to record kinematic variables for human motion analysis, since they are tools for easy access and portability for clinical use. Objective: The aim of this study was to describe and analyze the kinematics characteristics using the inertial sensor incorporated in the iPhone 4S, the lower limbs strength through a manual dynamometer, and the jump variables obtained with a contact mat in the squat jump and countermovement jump tests (fly time and jump height) from a cohort of healthy people. Methods: A cross sectional study was conducted on a population of healthy young adults. Twenty-seven participants performed three trials (n=81 jumps) of squat jump and countermovement jump tests. Acceleration variables were measured through a smartphone’s inertial sensor. Additionally, jump variables from a contact mat and lower limbs dynamometry were collected. Results: In the present study, the kinematic variables derived from acceleration through the inertial sensor of a smartphone iPhone 4S, dynamometry of lower limbs with a handheld dynamometer, and the height and flight time with a contact mat have been described in vertical jump tests from a cohort of young healthy subjects. The development of the execution has been described, examined and identified in a squat jump test and countermovement jump test under acceleration variables that were obtained with the smartphone. Conclusions: The built-in iPhone 4S inertial sensor is able to measure acceleration variables while performing vertical jump tests for the squat jump and countermovement jump in healthy young adults. The acceleration kinematics variables derived from the smartphone’s inertial sensor are higher in the countermovement jump test than the squat jump test. [JMIR Rehabil Assist Technol 2015;2(2):e7]