Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review.

Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson's correlation with an RPE (rate of perceived exertion) between r = 0.79 and r = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE.

Drift-Free 3D Orientation and Displacement Estimation for Quasi-Cyclical Movements Using One Inertial Measurement Unit: Application to Running.

A Drift-Free 3D Orientation and Displacement estimation method (DFOD) based on a single inertial measurement unit (IMU) is proposed and validated. Typically, body segment orientation and displacement methods rely on a constant- or zero-velocity point to correct for drift. Therefore, they are not easily applicable to more proximal segments than the foot. DFOD uses an alternative single sensor drift reduction strategy based on the quasi-cyclical nature of many human movements. DFOD assumes that the quasi-cyclical movement occurs in a quasi-2D plane and with an approximately constant cycle average velocity. DFOD is independent of a constant- or zero-velocity point, a biomechanical model, Kalman filtering or a magnetometer. DFOD reduces orientation drift by assuming a cyclical movement, and by defining a functional coordinate system with two functional axes. These axes are based on the mean acceleration and rotation axes over multiple complete gait cycles. Using this drift-free orientation estimate, the displacement of the sensor is computed by again assuming a cyclical movement. Drift in displacement is reduced by subtracting the mean value over five gait cycle from the free acceleration, velocity, and displacement. Estimated 3D sensor orientation and displacement for an IMU on the lower leg were validated with an optical motion capture system (OMCS) in four runners during constant velocity treadmill running. Root mean square errors for sensor orientation differences between DFOD and OMCS were 3.1 ± 0.4° (sagittal plane), 5.3 ± 1.1° (frontal plane), and 5.0 ± 2.1° (transversal plane). Sensor displacement differences had a root mean square error of 1.6 ± 0.2 cm (forward axis), 1.7 ± 0.6 cm (mediolateral axis), and 1.6 ± 0.2 cm (vertical axis). Hence, DFOD is a promising 3D drift-free orientation and displacement estimation method based on a single IMU in quasi-cyclical movements with many advantages over current methods.

How Are Sports-Trackers Used by Runners? Running-Related Data, Personal Goals, and Self-Tracking in Running.

The purpose of this research is to explore the roles that sports trackers and running-related data play in runners' personal goal achievement. A two-week diary study and semi-structured interviews were conducted with 22 runners to explore how runners engage with their running-related data to set and achieve their running goals. We found that participants pursued and transitioned between different running goals as their needs, abilities, and surrounding environment changed. We also found multiple motivations that shaped the use of sports trackers. We identified two main categories in runners' motivations for using trackers and data to achieve their goals. These categories were (i) documenting and tracking in running, and (ii) supporting goal-oriented reflections and actions, with various reasons for use while preparing for and during running. This study provides insights into the psychological effects of running-related data and signals practical implications for runners and developers of tracking technology.

Towards Machine Learning-Based Detection of Running-Induced Fatigue in Real-World Scenarios: Evaluation of IMU Sensor Configurations to Reduce Intrusiveness.

Physical fatigue is a recurrent problem in running that negatively affects performance and leads to an increased risk of being injured. Identification and management of fatigue helps reducing such negative effects, but is presently commonly based on subjective fatigue measurements. Inertial sensors can record movement data continuously, allowing recording for long durations and extensive amounts of data. Here we aimed to assess if inertial measurement units (IMUs) can be used to distinguish between fatigue levels during an outdoor run with a machine learning classification algorithm trained on IMU-derived biomechanical features, and what is the optimal configuration to do so. Eight runners ran 13 laps of 400 m on an athletic track at a constant speed with 8 IMUs attached to their body (feet, tibias, thighs, pelvis, and sternum). Three segments were extracted from the run: laps 2-4 (no fatigue condition, Rating of Perceived Exertion (RPE) = 6.0 ± 0.0); laps 8-10 (mild fatigue condition, RPE = 11.7 ± 2.0); laps 11-13 (heavy fatigue condition, RPE = 14.2 ± 3.0), run directly after a fatiguing protocol (progressive increase of speed until RPE ≥ 16) that followed lap 10. A random forest classification algorithm was trained with selected features from the 400 m moving average of the IMU-derived accelerations, angular velocities, and joint angles. A leave-one-subject-out cross validation was performed to assess the optimal combination of IMU locations to detect fatigue and selected sensor configurations were considered. The left tibia was the most recurrent sensor location, resulting in accuracies ranging between 0.761 (single left tibia location) and 0.905 (all IMU locations). These findings contribute toward a balanced choice between higher accuracy and lower intrusiveness in the development of IMU-based fatigue detection devices in running.

Manual physical balance assistance of therapists during gait training of stroke survivors: characteristics and predicting the timing.

BACKGROUND: During gait training, physical therapists continuously supervise stroke survivors and provide physical support to their pelvis when they judge that the patient is unable to keep his balance. This paper is the first in providing quantitative data about the corrective forces that therapists use during gait training. It is assumed that changes in the acceleration of a patient's COM are a good predictor for therapeutic balance assistance during the training sessions Therefore, this paper provides a method that predicts the timing of therapeutic balance assistance, based on acceleration data of the sacrum. METHODS: Eight sub-acute stroke survivors and seven therapists were included in this study. Patients were asked to perform straight line walking as well as slalom walking in a conventional training setting. Acceleration of the sacrum was captured by an Inertial Magnetic Measurement Unit. Balance-assisting corrective forces applied by the therapist were collected from two force sensors positioned on both sides of the patient's hips. Measures to characterize the therapeutic balance assistance were the amount of force, duration, impulse and the anatomical plane in which the assistance took place. Based on the acceleration data of the sacrum, an algorithm was developed to predict therapeutic balance assistance. To validate the developed algorithm, the predicted events of balance assistance by the algorithm were compared with the actual provided therapeutic assistance. RESULTS: The algorithm was able to predict the actual therapeutic assistance with a Positive Predictive Value of 87% and a True Positive Rate of 81%. Assistance mainly took place over the medio-lateral axis and corrective forces of about 2% of the patient's body weight (15.9 N (11), median (IQR)) were provided by therapists in this plane. Median duration of balance assistance was 1.1 s (0.6) (median (IQR)) and median impulse was 9.4Ns (8.2) (median (IQR)). Although therapists were specifically instructed to aim for the force sensors on the iliac crest, a different contact location was reported in 22% of the corrections. CONCLUSIONS: This paper presents insights into the behavior of therapists regarding their manual physical assistance during gait training. A quantitative dataset was presented, representing therapeutic balance-assisting force characteristics. Furthermore, an algorithm was developed that predicts events at which therapeutic balance assistance was provided. Prediction scores remain high when different therapists and patients were analyzed with the same algorithm settings. Both the quantitative dataset and the developed algorithm can serve as technical input in the development of (robot-controlled) balance supportive devices.

Paretic versus non-paretic stepping responses following pelvis perturbations in walking chronic-stage stroke survivors.

BACKGROUND: The effects of a stroke, such as hemiparesis, can severely hamper the ability to walk and to maintain balance during gait. Providing support to stroke survivors through a robotic exoskeleton, either to provide training or daily-life support, requires an understanding of the balance impairments that result from a stroke. Here, we investigate the differences between the paretic and non-paretic leg in making recovery steps to restore balance following a disturbance during walking. METHODS: We perturbed 10 chronic-stage stroke survivors during walking using mediolateral perturbations of various amplitudes. Kinematic data as well as gluteus medius muscle activity levels during the first recovery step were recorded and analyzed. RESULTS: The results show that this group of subjects is able to modulate foot placement in response to the perturbations regardless of the leg being paretic or not. Modulation in gluteus medius activity with the various perturbations is in line with this observation. In general, the foot of the paretic leg was laterally placed further away from the center of mass than that of the non-paretic leg, while subjects spent more time standing on the non-paretic leg. CONCLUSIONS: The findings suggest that, though stroke-related gait characteristics are present, the modulation with the various perturbations remains unaffected. This might be because all subjects were only mildly impaired, or because these stepping responses partly occur through involuntary pathways which remain unaffected by the complications after the stroke.

The effect of pressure and shear on tissue viability of human skin in relation to the development of pressure ulcers: a systematic review.

Pressure ulcers are a significant problem in health care, due to high costs and large impact on patients' life. In general, pressure ulcers develop as tissue viability decreases due to prolonged mechanical loading. The relation between load and tissue viability is highly influenced by individual characteristics. It is proposed that measurements of skin blood flow regulation could provide good assessment of the risk for pressure ulcer development, as skin blood flow is essential for tissue viability. . Therefore, the aim of this systematic review is to gain insight in the relation between mechanical load and the response of the skin and underlying tissue to this loading measured in-vivo with non-invasive techniques. A systematic literature search was performed to identify articles analysing the relation between mechanical load (pressure and/or shear) and tissue viability measured in-vivo. Two independent reviewers scored the methodological quality of the 22 included studies. Methodological information as well as tissue viability parameters during load application and after load removal were extracted from the included articles and used in a meta-analysis. Pressure results in a decrease in skin blood flow parameters, compared to baseline; showing a larger decrease with higher magnitudes of load. The steepness of the decrease is mostly dependent on the anatomical location. After load removal the magnitude of the post-reactive hyperaemic peak is related to the magnitude of pressure. Lastly, shear in addition to pressure, shows an additional negative effect, but the effect is less apparent than pressure on skin viability.

3D Tibial Acceleration and Consideration of 3D Angular Motion Using IMUs on Peak Tibial Acceleration and Impulse in Running.

PURPOSE: Peak tibial acceleration (PTA) is defined as the peak acceleration occurring shortly after initial contact, often used as an indirect measure of tibial load. As the tibia is a rotating segment around the ankle, angular velocity and angular acceleration should be included in PTA. This study aimed to quantify three-dimensional tibial acceleration components over two different sensor locations and three running speeds, to get a better understanding of the influence of centripetal and tangential accelerations on PTA typically measured in running. Furthermore, it explores tibial impulse as an alternative surrogate measure for tibial load. METHODS: Fifteen participants ran 90 s on a treadmill at 2.8, 3.3, and 3.9 m·s -1 , with inertial measurement units (IMUs) located distally and proximally on the tibia. RESULTS: Without the inclusion of rotational accelerations and gravity, no significant difference was found between axial PTA between both IMU locations, whereas in the tangential sagittal plane axis, there was a significant difference. Inclusion of rotational accelerations and gravity resulted in similar PTA estimates at the ankle for both IMU locations and caused a significant difference between PTA based on the distal IMU and PTA at the ankle. The impulse showed more consistent results between the proximal and distal IMU locations compared with axial PTA. CONCLUSIONS: Rotational acceleration of the tibia during stance differently impacted PTA measured proximally and distally at the tibia, indicating that rotational acceleration and gravity should be included in PTA estimates. Furthermore, peak acceleration values (such as PTA) are not always reliable when using IMUs because of inconsistent PTA proximally compared with distally on an individual level. Instead, impulse seems to be a more consistent surrogate measure for the tibial load.

Estimating 3D ground reaction forces in running using three inertial measurement units.

To understand the mechanisms causing running injuries, it is crucial to get insights into biomechanical loading in the runners' environment. Ground reaction forces (GRFs) describe the external forces on the body during running, however, measuring these forces is usually only possible in a gait laboratory. Previous studies show that it is possible to use inertial measurement units (IMUs) to estimate vertical forces, however, forces in anterior-posterior direction play an important role in the push-off. Furthermore, to perform an inverse dynamics approach, for modelling tissue specific loads, 3D GRFs are needed as input. Therefore, the goal of this work was to estimate 3D GRFs using three inertial measurement units. Twelve rear foot strike runners did nine trials at three different velocities (10, 12 and 14 km/h) and three stride frequencies (preferred and preferred ± 10%) on an instrumented treadmill. Then, data from IMUs placed on the pelvis and lower legs were used as input for artificial neural networks (ANNs) to estimate 3D GRFs. Additionally, estimated vertical GRF from a physical model was used as input to create a hybrid machine learning model. Using different splits in validation and training data, different ANNs were fitted and assembled into an ensemble model. Leave-one-subject-out cross-validation was used to validate the models. Performance of the machine learning, hybrid machine learning and a physical model were compared. The estimated vs. measured GRF for the hybrid model had a RMSE normalized over the full range of values of 10.8, 7.8 and 6.8% and a Pearson correlation coefficient of 0.58, 0.91, 0.97 for the mediolateral direction, posterior-anterior and vertical direction respectively. Performance for the three compared models was similar. The ensemble models showed higher model accuracy compared to the ensemble-members. This study is the first to estimate 3D GRF during continuous running from IMUs and shows that it is possible to estimate GRF in posterior-anterior and vertical direction, making it possible to estimate these forces in the outdoor setting. This step towards quantification of biomechanical load in the runners' environment is helpful to gain a better understanding of the development of running injuries.

Quantifying and correcting for speed and stride frequency effects on running mechanics in fatiguing outdoor running.

Measuring impact-related quantities in running is of interest to improve the running technique. Many quantities are typically measured in a controlled laboratory setting, even though most runners run in uncontrolled outdoor environments. While monitoring running mechanics in an uncontrolled environment, a decrease in speed or stride frequency can mask fatigue-related changes in running mechanics. Hence, this study aimed to quantify and correct the subject-specific effects of running speed and stride frequency on changes in impact-related running mechanics during a fatiguing outdoor run. Seven runners ran a competitive marathon while peak tibial acceleration and knee angles were measured with inertial measurement units. Running speed was measured through sports watches. Median values over segments of 25 strides throughout the marathon were computed and used to create subject-specific multiple linear regression models. These models predicted peak tibial acceleration, knee angles at initial contact, and maximum stance phase knee flexion based on running speed and stride frequency. Data were corrected for individual speed and stride frequency effects during the marathon. The speed and stride frequency corrected and uncorrected data were divided into ten stages to investigate the effect of marathon stage on mechanical quantities. This study showed that running speed and stride frequency explained, on average, 20%-30% of the variance in peak tibial acceleration, knee angles at initial contact, and maximum stance phase knee angles while running in an uncontrolled setting. Regression coefficients for speed and stride frequency varied strongly between subjects. Speed and stride frequency corrected peak tibial acceleration, and maximum stance phase knee flexion increased throughout the marathon. At the same time, uncorrected maximum stance phase knee angles showed no significant differences between marathon stages due to a decrease in running speed. Hence, subject-specific effects of changes in speed and stride frequency influence the interpretation of running mechanics and are relevant when monitoring, or comparing the gait pattern between runs in uncontrolled environments.

Perspective on "in the wild" movement analysis using machine learning.

Recent advances in wearable sensing and machine learning have created ample opportunities for "in the wild" movement analysis in sports, since the combination of both enables real-time feedback to be provided to athletes and coaches, as well as long-term monitoring of movements. The potential for real-time feedback is useful for performance enhancement or technique analysis, and can be achieved by training efficient models and implementing them on dedicated hardware. Long-term monitoring of movement can be used for injury prevention, among others. Such applications are often enabled by training a machine learned model from large datasets that have been collected using wearable sensors. Therefore, in this perspective paper, we provide an overview of approaches for studies that aim to analyze sports movement "in the wild" using wearable sensors and machine learning. First, we discuss how a measurement protocol can be set up by answering six questions. Then, we discuss the benefits and pitfalls and provide recommendations for effective training of machine learning models from movement data, focusing on data pre-processing, feature calculation, and model selection and tuning. Finally, we highlight two application domains where "in the wild" data recording was combined with machine learning for injury prevention and technique analysis, respectively.

Effects of level running-induced fatigue on running kinematics: A systematic review and meta-analysis.

BACKGROUND: Runners have a high risk of acquiring a running-related injury. Understanding the mechanisms of impact force attenuation into the body when a runner fatigues might give insight into the role of running kinematics on the aetiology of overuse injuries. RESEARCH QUESTIONS: How do running kinematics change due to running-induced fatigue? And what is the influence of experience level on changes in running kinematics due to fatigue? METHODS: Three electronic databases were searched: PubMed, Web of Science, and Scopus. This resulted in 33 articles and 19 kinematic quantities being included in this review. A quality assessment was performed on all included articles and meta-analyses were performed for 18 kinematic quantities. RESULTS AND SIGNIFICANCE: Main findings included an increase in peak acceleration at the tibia and a decrease in leg stiffness after a fatiguing protocol. Additionally, level running-induced fatigue increased knee flexion at initial contact and maximum knee flexion during swing. An increase in vertical centre of mass displacement was found in novice but not in experienced runners with fatigue. Overall, runners changed their gait pattern due to fatigue by moving to a smoother gait pattern (i.e. more knee flexion at initial contact and during swing, decreased leg stiffness). However, these changes were not sufficient to prevent an increase in peak accelerations at the tibia after a fatigue protocol. Large inter-individual differences in responses to fatigue were reported. Hence, it is recommended to investigate changes in running kinematics as a result of fatigue on a subject-specific level since group-level analysis might mask individual responses.

Peak tibial acceleration should not be used as indicator of tibial bone loading during running.

Peak tibial acceleration (PTA) is a widely used indicator of tibial bone loading. Indirect bone loading measures are of interest to reduce the risk of stress fractures during running. However, tibial compressive forces are caused by both internal muscle forces and external ground reaction forces. PTA might reflect forces from outside the body, but likely not the compressive force from muscles on the tibial bone. Hence, the strength of the relationship between PTA and maximum tibial compression forces in rearfoot-striking runners was investigated. Twelve runners ran on an instrumented treadmill while tibial acceleration was captured with accelerometers. Force plate and inertial measurement unit data were spatially aligned with a novel method based on the centre of pressure crossing a virtual toe marker. The correlation coefficient between maximum tibial compression forces and PTA was 0.04 ± 0.14 with a range of -0.15 to +0.28. This study showed a very weak and non-significant correlation between PTA and maximum tibial compression forces while running on a level treadmill at a single speed. Hence, PTA as an indicator for tibial bone loading should be reconsidered, as PTA does not provide a complete picture of both internal and external compressive forces on the tibial bone. .

Detection of foot contact in treadmill running with inertial and optical measurement systems.

In running assessments, biomechanics of the stance phase are often measured to understand external loads applied to the body. Identifying time of initial foot contact can be challenging in runners with different strike patterns. Peak downward velocity of the pelvis (PDVP) has been validated in a laboratory setting to detect initial contact. Inertial measurement units (IMUs) allow measurements of kinematic variables outside laboratory settings. The aim of this study was to validate the PDVP method using an inertial and optical motion capture system to detect initial contact at different speeds and foot strike patterns compared to the force sensing criterion. Twenty healthy runners ran for two minutes at 11, 13, and 15 km/h on a force-instrumented treadmill. 3D kinematics were obtained from an optical motion capture system and an 8-sensor inertial system. A generalized estimating equation showed no effect of footstrike pattern on the time difference (offset) between initial contact based on an inertial or optical system and the force sensing criterion. There was a significant main effect of speed on offset, in which offsets decreased with higher speeds. There was no interaction effect of speed and foot strike pattern on the offsets. Offsets ranged from 21.7 ± 0.2 ms for subjects running at 15 km/h (inertial versus force sensing criterion) to 27.2 ± 0.1 ms for subjects running at 11 km/h (optical versus force sensing criterion). These findings support the validity of the PDVP method obtained from optical and inertial systems to detect initial contact in different footstrike patterns and at different running speeds.

Clinical use of interface pressure to predict pressure ulcer development: a systematic review.

Pressure ulcers are a large problem in subjects who use a wheelchair for their mobility. These ulcers originate beneath the bony prominences of the pelvis and progress outward as a consequence of prolonged pressure. Interface pressure is used clinically to predict and prevent pressure ulcers. However, the relation between interface pressure and the development of pressure ulcers is not clear. A systematic review was performed to address the research question of whether interface pressure can be used to predict the development of pressure ulcers or to determine the prognosis of an ulcer once developed. Seven studies were identified that measured interface pressure and used the development or healing of pressure ulcers as an outcome measure. There appears to be a weak qualitative relation between interface pressure and the development of pressure ulcers. However, no conclusive clinical threshold for the interface pressure can be given. This, combined with the influence of individual characteristics, the low internal validity of interface pressure measurement for the prediction of pressure ulcers, and an incongruent relation between pressure magnitude and duration, results in the fact that no quantification of the predictive or prognostic value of interface pressure can be given.

Kinematics and shock attenuation during a prolonged run on the athletic track as measured with inertial magnetic measurement units.

BACKGROUND: Tibial stress fractures are common running related injury and their etiology may include biomechanical factors like impact forces, shock attenuation, lower limb kinematics and how these factors are influenced by intense or prolonged running. Inertial-magnetic measurement units (IMUs) have recently emerged as an alternative to motion capture but their use to date was mostly limited to segmental and joint motion. RESEARCH QUESTION: The present study sought to examine the effects of a prolonged run on shock attenuation, peak tibial and sacral acceleration (PTA, PSA), and lower limb kinematics using IMUs. METHODS: Ten trained male runners (31 +/- 5 yr, 183 +/- 3 cm, 76 +/- 9 kg) performed a twenty-minute prolonged run on an athletic track at estimated lactate threshold speed. Eight IMUs, positioned over the feet, lower and uppers legs, sacrum and sternum, were used to calculate joint kinematics, impact parameters and shock attenuation in the time domain (1-(PSA/PTA)*100). RESULTS: PTA increased while PSA and shock attenuation did not change following the prolonged run. Hip and knee flexion at midstance decreased. Vertical lower leg angle at initial contact did not change. CONCLUSION: By using IMUs, it was shown that a prolonged run at estimated lactate threshold speed had significant effects on kinematics and tibial acceleration parameters. By modifying hip and knee joint kinematics during stance, the body was able to maintain sacral acceleration possibly by shifting from active shock attenuation to more passive mechanisms. SIGNIFICANCE: The present study shows that inertial sensors can be used in outdoor running to measure joint kinematics and kinetic parameters like PTA, PSA and shock attenuation simultaneously. The results of this study show new insights into how the body copes with impact during prolonged running.

Effects of sagittal postural adjustments on seat reaction load.

Wheelchair-dependent subjects often adopt a passive sitting posture and suffer from sitting acquired pressure ulcers (PU) that mainly occur when high buttock pressures sustain for a longer period of time. Body posture directly influences seating load and proper postural change is therefore essential. Many subjects cannot reposition themselves and seating systems that adjust sitting posture can regulate seating load and potentially diminish the risk to develop PU. We describe a mechanism for postural adjustments and investigated the influence of seat inclination (1), pelvis rotation (2) and chair recline (3) on buttock load. We developed an experimental simulator chair containing the concept to adjust body posture in the sagittal plane. Measurements involved quasi-static actuated chair movements in which chair configuration, buttock interface pressure and seating forces were simultaneously measured. Significant effects with high coefficients of determination (r(2)>0.8) were found for seating force, centre of pressure and sacral load. We conclude that a combination of independent pelvis rotation and seat inclination is effective to regulate the net buttock shear force and the sacral interface pressure in healthy subjects. Whether both techniques are also applicable in subjects without postural stability is still to be evaluated.

Body segments decoupling in sitting: control of body posture from automatic chair adjustments.

BACKGROUND: Individuals who cannot functionally reposition themselves adopt a passive body posture and suffer from physical discomfort in long-term sitting. To regulate body load and to prevent sitting related mobility problems, proper posture control is important. The inability to reposition underlines the importance for seating interventions that control body posture from automatic chair adjustments. We developed an adjustable simulator chair that allows the alignment of the trunk, pelvis and thighs to be controlled independently. This study describes the system for decoupled body segments adjustment and develops a predictive model that computes angular chair configuration for desired body postures. METHODS: Eighteen healthy male subjects participated in this study. The experiment involved a protocol of five trials, each investigating the effect of individual chair segment angle adjustment on body segments rotation. Quasi-static chair adjustments were performed, in which angular chair configuration and body segments orientation were measured using an infrared motion capturing system and an inertia sensor attached on the pelvis. RESULTS: Linear best-fit equations together with the coefficients of determination were computed. Significant relations have been found between angular chair configuration and body segments orientation leading to an algorithm that predicts chair configuration for desired body posture. CONCLUSIONS: The predictive algorithm seems applicable to compute angular chair configuration for desired body posture when the initial body-chair configuration is known. For clinical application, future experiments must be performed on impaired individuals to validate the algorithm in terms of accuracy.

Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors.

Analysis of running mechanics has traditionally been limited to a gait laboratory using either force plates or an instrumented treadmill in combination with a full-body optical motion capture system. With the introduction of inertial motion capture systems, it becomes possible to measure kinematics in any environment. However, kinetic information could not be provided with such technology. Furthermore, numerous body-worn sensors are required for a full-body motion analysis. The aim of this study is to examine the validity of a method to estimate sagittal knee joint angles and vertical ground reaction forces during running using an ambulatory minimal body-worn sensor setup. Two concatenated artificial neural networks were trained (using data from eight healthy subjects) to estimate the kinematics and kinetics of the runners. The first artificial neural network maps the information (orientation and acceleration) of three inertial sensors (placed at the lower legs and pelvis) to lower-body joint angles. The estimated joint angles in combination with measured vertical accelerations are input to a second artificial neural network that estimates vertical ground reaction forces. To validate our approach, estimated joint angles were compared to both inertial and optical references, while kinetic output was compared to measured vertical ground reaction forces from an instrumented treadmill. Performance was evaluated using two scenarios: training and evaluating on a single subject and training on multiple subjects and evaluating on a different subject. The estimated kinematics and kinetics of most subjects show excellent agreement (ρ>0.99) with the reference, for single subject training. Knee flexion/extension angles are estimated with a mean RMSE <5°. Ground reaction forces are estimated with a mean RMSE < 0.27 BW. Additionaly, peak vertical ground reaction force, loading rate and maximal knee flexion during stance were compared, however, no significant differences were found. With multiple subject training the accuracy of estimating discrete and continuous outcomes decreases, however, good agreement (ρ > 0.9) is still achieved for seven of the eight different evaluated subjects. The performance of multiple subject learning depends on the diversity in the training dataset, as differences in accuracy were found for the different evaluated subjects.

Performance of a visuomotor walking task in an augmented reality training setting.

Visual cues can be used to train walking patterns. Here, we studied the performance and learning capacities of healthy subjects executing a high-precision visuomotor walking task, in an augmented reality training set-up. A beamer was used to project visual stepping targets on the walking surface of an instrumented treadmill. Two speeds were used to manipulate task difficulty. All participants (n = 20) had to change their step length to hit visual stepping targets with a specific part of their foot, while walking on a treadmill over seven consecutive training blocks, each block composed of 100 stepping targets. Distance between stepping targets was varied between short, medium and long steps. Training blocks could either be composed of random stepping targets (no fixed sequence was present in the distance between the stepping targets) or sequenced stepping targets (repeating fixed sequence was present). Random training blocks were used to measure non-specific learning and sequenced training blocks were used to measure sequence-specific learning. Primary outcome measures were performance (% of correct hits), and learning effects (increase in performance over the training blocks: both sequence-specific and non-specific). Secondary outcome measures were the performance and stepping-error in relation to the step length (distance between stepping target). Subjects were able to score 76% and 54% at first try for lower speed (2.3 km/h) and higher speed (3.3 km/h) trials, respectively. Performance scores did not increase over the course of the trials, nor did the subjects show the ability to learn a sequenced walking task. Subjects were better able to hit targets while increasing their step length, compared to shortening it. In conclusion, augmented reality training by use of the current set-up was intuitive for the user. Suboptimal feedback presentation might have limited the learning effects of the subjects.