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Accurately predicting physical activity-associated mechanical loading is crucial for developing and monitoring exercise interventions that improve bone health. While accelerometer-based prediction equations offer a promising solution, their external validity across different populations and activity contexts remains unclear. This study aimed to validate existing mechanical loading prediction equations by applying them to a sample and testing conditions distinct from the original validation studies. A convenience sample of 49 adults performed walking, running, and jumping activities on a force plate while wearing accelerometers at their hip, lower back, and ankle. Peak ground reaction force (pGRF) and peak loading rate (pLR) predictions were assessed for accuracy. Substantial variability in prediction accuracy was found, with pLR showing the highest errors. These findings highlight the need to improve prediction models to account for individual biomechanical differences, sensor placement, and high-impact activities. Such refinements are essential for ensuring the models' reliability in real-world applications, particularly in clinical and biomechanical research contexts, where accurate assessments of mechanical loading are critical for designing rehabilitation programs, injury prevention strategies, and optimizing bone health interventions. [ABSTRACT FROM AUTHOR]

This study used a 12-week plyometric and strength training program as an intervention to improve upper- and lower-extremity muscle strength for jumping and landing when climbing high walls. Sixty general non-athlete male college students were openly recruited and divided into an experimental group and a control group. The experimental group underwent a plyometric and strength training program twice a week for 12 weeks (24 sessions). The intervention was divided into three phases, each lasting four weeks, with the training intensity gradually increasing in each phase. A hand grip dynamometer was used to measure grip strength, and a PASCO double-track force plate was used to assess upper-extremity push-up force and lower-extremity take-off and landing strength. The results of the 12-week intervention showed that the experimental group experienced significant increases in grip strength (both hands), hand-ground reaction force, and upper-extremity hang time. Additionally, the time of upper-extremity action on the force plate decreased. Lower-extremity take-off strength improved, as reflected in increased ground reaction force, rate of force development, and passage time. Upon landing, ground reaction force decreased by 3.2%, and cushioning time shortened by 52.7%. This study concludes that plyometric and strength training have promising effects in enhancing upper- and lower-extremity strength, particularly in climbing and landing tasks. [ABSTRACT FROM AUTHOR]

In this study, we developed a lightweight shoe sensor system equipped with four high-capacity, compact triaxial force sensors and an inertial measurement unit. Remarkably, this system enabled measurements of localized three-directional ground reaction forces (GRFs) at each sensor position (heel, first and fifth metatarsal heads, and toe) and estimations of stride length and toe clearance during walking. Compared to conventional optical motion analysis systems, the developed sensor system provided relatively accurate results for stride length and minimum toe clearance. To test the performance of the system, 15 older and 8 young adults were instructed to walk along a straight line while wearing the system. The results reveal that compared to the young adults, older adults exhibited lower localized GRF contributions from the heel and greater localized GRF contribution from the toe and fifth metatarsal locations. Furthermore, the older adults exhibited greater variability in their stride length and smaller toe clearance with greater variability compared to the young adults. These results underscore the effectiveness of the proposed gait analysis system in distinguishing the gait characteristics of young and older adults, potentially replacing traditional motion capture systems and force plates in gait analysis. [ABSTRACT FROM AUTHOR]

Existing studies on gait phase estimation generally involve walking experiments using inertial measurement units under limited walking conditions (WCs). In this study, a gait phase estimation algorithm is proposed that uses data from force sensing resistors (FSRs) and a Bi-LSTM model. The proposed algorithm estimates gait phases in real time under various WCs, e.g., walking on paved/unpaved roads, ascending and descending stairs, and ascending or descending on ramps. The performance of the proposed algorithm is evaluated by performing walking experiments on ten healthy adult participants. An average gait estimation accuracy exceeding 90% is observed with a small error (root mean square error = 0.794, R2 score = 0.906) across various WCs. These results demonstrate the wide applicability of the proposed gait phase estimation algorithm using various insole devices, e.g., in walking aid control, gait disturbance diagnosis in daily life, and motor ability analysis. [ABSTRACT FROM AUTHOR]

Body balancing is a complex task that includes the coordination of muscles, tendons, bones, ears, eyes, and the brain. Imbalance or disequilibrium is the inability to maintain the center of gravity. Perpetuating body balance plays an important role in preventing us from falling or swaying. Biomechanical tests and video analysis can be performed to analyze body imbalance. The musculoskeletal system is one of the fundamental systems by which our balance or equilibrium is sustained and our upright posture is maintained. Electromyogram (EMG) and ground reaction force (GRF) monitoring can be utilized in cases where a rapid response to body imbalance is a necessity. Body balance also depends on visual stimuli that can be either real or virtual. Researchers have used virtual reality (VR) to predict motion sickness and analyze heart rate variability, as well as in rehabilitation. VR can also be used to induce body imbalance in a controlled way. In this research, body imbalance was induced in a controlled way by playing an Oculus game and, simultaneously, EMG and GRF were recorded. Features were extracted from the EMG and were then fed to a machine learning algorithm. Several machine learning algorithms were tested and upon 10-fold cross-validation; a minimum accuracy of 71% and maximum accuracy of 98% were achieved by Gaussian Naïve Bayes and Gradient Boosting classifiers, respectively, in the classification of imbalance and its intensities. This research can be incorporated into various rehabilitative and therapeutic systems. [ABSTRACT FROM AUTHOR]

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This approach can be utilized to improve a user's rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques. [ABSTRACT FROM AUTHOR]

Previous research addressed the spatiotemporal variables of the drop jump (DJ) versus the horizontal drop jump (HDJ). This study compared the kinetic variables of the DJ versus the HDJ in elite jumpers and sprinters. In a single session, sixteen elite jumpers and sprinters performed two DJ attempts with three different fall heights (0.30 m, 0.40 m, and 0.50 m), and after 2 h, performed two HDJ attempts from the same fall heights (0.30 m, 0.40 m, and 0.50 m). Kinetic variables: eccentric ground reaction forces (GRFE) and concentric ground reaction forces; eccentric impulse (PE) and concentric impulse (PC); peak power in the concentric phase; and rate of force decrease (RFDe) were measured using a research-grade force plate. The Wilcoxon test was used to compare the vertical and anteroposterior axes. GRFE was significantly higher (p ≤ 0.05) in the DJ vs the HDJ with large effect sizes. The PE (p ≤ 0.006) and PC (p = 0.002) were significantly lower in the DJ than in the HDJ. The RFDe was also significantly lower in the DJ at 0.30 m vs. the HDJ at 0.30 m (p = 0.002). In summary, elite jumpers and sprinters may benefit from incorporating both the DJ and the HDJ into their training regimens, with the DJ being particularly advantageous for enhancing power metrics and RFDe. [ABSTRACT FROM AUTHOR]

After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population. [ABSTRACT FROM AUTHOR]

Morton's foot syndrome (MFS) is characterized by a distally longer head of the second metatarsal bone compared to the head of the first metatarsal bone. Few studies have investigated the effects of a foot orthosis on kinetic characteristics, such as ground reaction force (GRF), during walking in individuals with MFS. This study aimed to verify dynamic GRF using a 3D motion analysis system, including two platforms with and without a foot orthosis condition. Kinetic GRF data of 26 participants with MFS were collected using a motion analysis system and a force platform. Participants were asked to walk wearing standard shoes or shoes with a pad-type foot orthosis. Repeated-measures analysis of variance (ANOVA) was used to compare the kinetic GRF data in the stance phase during gait according to the side of the leg and orthotic conditions for MFS. The late sagittal and frontal peak forces showed that the presence of a foot orthosis condition significantly increased the GRF when compared with the absence of a foot orthosis condition for both sides of the feet (p < 0.05). In addition, the second vertical peak force of the GRF showed that the presence of a foot orthosis condition significantly increased the GFR when compared with the absence of a foot orthosis condition on the side of the right foot (p = 0.023). Significant effects were observed in the late sagittal and frontal peak GRFs when wearing the pad-type foot orthosis in individuals with MFS during gait. Thus, even if there are no signs and symptoms of MFS in patients diagnosed with the disease condition, clinical interventions, such as a foot orthosis, that can be simply applied to shoe insoles are needed to manage and prevent various musculoskeletal disorders that may develop in the future. It was hypothesized that when wearing a foot orthosis, the participants would walk with increased GRF during gait compared to those without an orthosis. [ABSTRACT FROM AUTHOR]

This study investigates the influence of the quality of the "deep squat" movement, adapted from the Functional Movement Screen (FMS) system, on the lower extremity movement pattern during baseball throwing, and its potential impact on throwing performance and propulsion efficiency. Twenty-two baseball players were recruited and categorized into two groups: 13 in the high-score squat group (HSS) and 9 in the low-score squat group (LSS), based on their deep squat screening results. This research explored disparities in ball velocity, propulsion efficiency, propulsion ground reaction force (GRF) characteristics, and throwing kinematics between these two groups. The findings revealed no significant difference in ball velocity between the groups. However, the LSS group demonstrated a lower propulsion GRF efficiency (p < 0.030, ES = 0.46), along with a higher vertical peak GRF (p < 0.002, ES = 0.66). In the pivot leg, the HSS group exhibited significantly lower impulse forces in the Impulse Fresultant (p < 0.035, ES = 0.45), throwing direction (p < 0.049, ES = 0.42), and vertical direction (p < 0.048, ES = 0.42). Additionally, the contribution to the ball velocity of the pivot leg was significantly greater in the HSS group, along with significantly better efficiency in Impulse Fresultant (p < 0.035, ES = 0.45), throwing direction (p < 0.053, ES = 0.41), and vertical direction (p < 0.032, ES = 0.46). In the leading leg, the HSS group demonstrated significantly lower impulse forces in the Impulse Fresultant (p < 0.001, ES = 0.69), throwing direction (p < 0.007, ES = 0.58), and vertical direction (p < 0.001, ES = 0.70). Moreover, the contribution to the ball velocity of the leading leg was significantly greater in the HSS group, accompanied by significantly better efficiency in Impulse Fresultant (p < 0.003, ES = 0.63), throwing direction (p < 0.005, ES = 0.60), and vertical direction (p < 0.021, ES = 0.49). In conclusion, this study suggests that squat screening is a valuable tool for assessing propulsion efficiency. Coaches and trainers should be mindful of players with low squat quality but high throwing performance, as they may face increased impact and injury risks in the future. [ABSTRACT FROM AUTHOR]

The magnitude and direction of inter-limb asymmetries in a change of direction (COD) have increased interest in scientific research in recent years. This present study aimed to investigate the magnitude of asymmetries in an elite youth female basketball sample (n = 18, age = 17.79 ± 0.67 y) and determine its directionality using force platform technology. Participants performed 70° and 180° COD tests analyzing the following variables: time, ground contact time (GCT) and ground reaction forces (GRF) along the anterior–posterior, mediolateral, and vertical axes. Inter-limb asymmetries were evident in both COD tests, with substantial differences observed between limbs (p < 0.01). The asymmetry values ranged from 3.02% to 24.31% in COD 180° and from 1.99% to 21.70% in COD 70°, with anterior–posterior GRF consistently exhibiting the highest asymmetry magnitude. Additionally, the directionality exhibited variability between the tests, indicating poor agreement and suggesting the independent directionality of asymmetries across tasks. Moreover, players required more time to complete the COD 180°, the GCT was noticeably longer for the COD 180° than for the COD 70°, and GRF varied across the axis, suggesting that players adapt uniquely to the specific demands of each task. The utilization of force platforms presents a comprehensive approach to assess asymmetries and COD variables performance variables which are "angle-dependent", which could have important implications for COD screening and effective training interventions. [ABSTRACT FROM AUTHOR]

We developed a shoe sole sensor system with four high-capacity, compact triaxial force sensors using a nitrogen added chromium strain-sensitive thin film mounted on the sole of a shoe. Walking experiments were performed, including straight walking and turning (side-step and cross-step turning), in six healthy young male participants and two healthy young female participants wearing the sole sensor system. A regression model to predict three-directional ground reaction forces (GRFs) from force sensor outputs was created using multiple linear regression and Gaussian process regression (GPR). The predicted GRF values were compared with the GRF values measured with a force plate. In the model trained on data from the straight walking and turning trials, the percent root-mean-square error (%RMSE) for predicting the GRFs in the anteroposterior and vertical directions was less than 15%, except for the GRF in the mediolateral direction. The model trained separately for straight walking, side-step turning, and cross-step turning showed a %RMSE of less than 15% in all directions in the GPR model, which is considered accurate for practical use. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate associations between peak magnitudes of raw acceleration (g) from wrist- and hip-worn accelerometers and ground reaction force (GRF) variables in a large sample of children and adolescents. A total of 269 participants (127 boys, 142 girls; age: 12.3 ± 2.0 yr) performed walking, running, jumping (<5 cm; >5 cm) and single-leg hopping on a force plate. A GENEActiv accelerometer was worn on the left wrist, and an Actigraph GT3X+ was worn on the right wrist and hip throughout. Mixed-effects linear regression was used to assess the relationships between peak magnitudes of raw acceleration and loading. Raw acceleration from both wrist and hip-worn accelerometers was strongly and significantly associated with loading (all p's < 0.05). Body mass and maturity status (pre/post-PHV) were also significantly associated with loading, whereas age, sex and height were not identified as significant predictors. The final models for the GENEActiv wrist, Actigraph wrist and Actigraph hip explained 81.1%, 81.9% and 79.9% of the variation in loading, respectively. This study demonstrates that wrist- and hip-worn accelerometers that output raw acceleration are appropriate for use to monitor the loading exerted on the skeleton and are able to detect short bursts of high-intensity activity that are pertinent to bone health. [ABSTRACT FROM AUTHOR]

The loadsol® wireless in-shoe force sensors can be useful for in-field measurements. However, its accuracy is unknown in the military context, whereby soldiers have to carry heavy loads and walk in military boots. The purpose of this study was to establish the validity of the loadsol® sensors in military personnel during loaded walking on flat, inclined and declined surfaces. Full-time Singapore Armed Forces (SAF) personnel (n = 8) walked on an instrumented treadmill on flat, 10° inclined, and 10° declined gradients while carrying heavy loads (25 kg and 35 kg). Normal ground reaction forces (GRF), perpendicular to the contact surface, were simultaneously measured using both the loadsol® sensors inserted in the military boots and the Bertec instrumented treadmill as the gold standard. A total of eight variables of interest were compared between loadsol® and treadmill, including four kinetic (impact peak force, active peak force, impulse, loading rate) and four spatiotemporal (stance time, stride time, cadence, step length) variables. Validity was assessed using Bland–Altman plots and 95% Limits of Agreement (LoA). Bias was calculated as the mean difference between the values obtained from loadsol® and the instrumented treadmill. Results showed similar force-time profiles between loadsol® sensors and the instrumented treadmill. The bias of most variables was generally low, with a narrow range of LoA. The high accuracy and good agreement with standard laboratory equipment suggest that the loadsol® system is a valid tool for measuring normal GRF during walking in military boots under heavy load carriage. [ABSTRACT FROM AUTHOR]

The ground reaction force (GRF) recorded by a platform when a person stands upright lies at the interface between the neural networks controlling stance and the body sway deduced from centre of pressure (CoP) displacement. It can be decomposed into vertical (VGRF) and horizontal (HGRF) vectors. Few studies have addressed the modulation of the GRFs by the sensory conditions and their relationship with body sway. We reconsidered the features of the GRFs oscillations in healthy young subjects (n = 24) standing for 90 s, with the aim of characterising the possible effects of vision, support surface and adaptation to repeated trials, and the correspondence between HGRF and CoP time-series. We compared the frequency spectra of these variables with eyes open or closed on solid support surface (EOS, ECS) and on foam (EOF, ECF). All stance trials were repeated in a sequence of eight. Conditions were randomised across different days. The oscillations of the VGRF, HGRF and CoP differed between each other, as per the dominant frequency of their spectra (around 4 Hz, 0.8 Hz and <0.4 Hz, respectively) featuring a low-pass filter effect from VGRF to HGRF to CoP. GRF frequencies hardly changed as a function of the experimental conditions, including adaptation. CoP frequencies diminished to <0.2 Hz when vision was available on hard support surface. Amplitudes of both GRFs and CoP oscillations decreased in the order ECF > EOF > ECS ≈ EOS. Adaptation had no effect except in ECF condition. Specific rhythms of the GRFs do not transfer to the CoP frequency, whereas the magnitude of the forces acting on the ground ultimately determines body sway. The discrepancies in the time-series of the HGRF and CoP oscillations confirm that the body's oscillation mode cannot be dictated by the inverted pendulum model in any experimental conditions. The findings emphasise the robustness of the VGRF "postural rhythm" and its correspondence with the cortical theta rhythm, shed new insight on current principles of balance control and on understanding of upright stance in healthy and elderly people as well as on injury prevention and rehabilitation. [ABSTRACT FROM AUTHOR]

Background: Despite the growing interest in golf, the impact of motion control strategy on golf performance may be uncertain. A network meta-analysis was conducted to investigate the relationships between ground reaction force (GRF), foot positions, and club types in golf and determine whether different clubs have a different impact when swinging. Methods: Three databases were searched from the time of inception to March 2023. Eligibility criteria included studies that provided data using a driver to swing and compared outcomes to control data. Results: Searches yielded 6527 studies. Seven studies met the selection criteria (n = 422 participants). The results showed that an iron 6 is the best when considering the trail foot vertical ground reaction force (TVGRF), trail foot anteroposterior ground reaction force (TAGRF), and lead foot vertical ground reaction force (LVGRF). The pitching wedge was the best in the lead foot mediolateral ground reaction force (LMGRF) and lead foot anteroposterior ground reaction force (LAGRF). Iron 7 was the best in the trail foot mediolateral ground reaction force (TMGRF), and the lead foot was larger than the trail foot to the vertical GRF. Discussion: The study found that clubs may influence a player's posture and swing power because golf clubs are available in various lengths and shapes. The lead foot generates a larger GRF than the trail foot; three-dimensional GRFs differ among golf clubs. When a golfer aims to maximize the distance of their drives, they must generate relatively more resultant horizontal reaction force (RFH). Golfers often use different clubs to achieve optimal performance on the course by controlling their motion. However, there needs to be a focus on the quality of the included studies because the sample size was too small, increasing the risk of bias associated with the results. [ABSTRACT FROM AUTHOR]

Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention. [ABSTRACT FROM AUTHOR]

Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or removed from the database. We formulated force platform-based re-ID as a deep metric learning (DML) task, whereby a deep neural network learns a feature representation that can be compared between inputs using a distance metric. The force platform dataset used in this study is one of the largest and the most comprehensive of its kind, containing 193 IDs with significant variations in clothing, footwear, walking speed, and time between trials. Several DML model architectures were evaluated in a challenging setting where none of the IDs were seen during training (i.e., zero-shot re-ID) and there was only one prior sample per ID to compare with each query sample. The best architecture was 85% accurate in this setting, though an analysis of changes in walking speed and footwear between measurement instances revealed that accuracy was 28% higher on same-speed, same-footwear comparisons, compared to cross-speed, cross-footwear comparisons. These results demonstrate the potential of DML algorithms for zero-shot re-ID using force platform data, and highlight challenging cases. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop peak ground reaction force (pGRF) and peak loading rate (pLR) prediction equations for high-impact activities in adult subjects with a broad range of body masses, from normal weight to severe obesity. A total of 78 participants (27 males; 82.4 ± 20.6 kg) completed a series of trials involving jumps of different types and heights on force plates while wearing accelerometers at the ankle, lower back, and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland–Altman plots. Body mass was a predictor in all models, along with peak acceleration in the pGRF models and peak acceleration rate in the pLR models. The equations to predict pGRF had a coefficient of determination (R2) of at least 0.83, and a mean absolute percentage error (MAPE) below 14.5%, while the R2 for the pLR prediction equations was at least 0.87 and the highest MAPE was 24.7%. Jumping pGRF can be accurately predicted through accelerometry data, enabling the continuous assessment of mechanical loading in clinical settings. The pLR prediction equations yielded a lower accuracy when compared to the pGRF equations. [ABSTRACT FROM AUTHOR]

Traditionally, the biomechanical analysis of Olympic weightlifting movements required laboratory equipment such as force platforms and transducers, but such methods are difficult to implement in practice. This study developed a field-based method using wearable technology and videos for the biomechanical assessment of weightlifters. To demonstrate the practicality of our method, we collected kinetic and kinematic data on six Singapore National Olympic Weightlifters. The participants performed snatches at 80% to 90% of their competition one-repetition maximum, and the three best attempts were used for the analysis. They wore a pair of in-shoe force sensors loadsol® (novel, Munich, Germany) to measure the vertical ground reaction forces under each foot. Concurrently, a video camera recorded the barbell movement from the side. The kinematics (e.g., trajectories and velocities) of the barbell were extracted using a free video analysis software (Kinovea). The power–time history was calculated from the force and velocity data. The results showed differences in power, force, and barbell velocity with moderate to almost perfect reliability. Technical inconsistency in the barbell trajectories were also identified. In conclusion, this study presented a simple and practical approach to evaluating weightlifters using in-shoe wearable sensors and videos. Such information can be useful for monitoring progress, identifying errors, and guiding training plans for weightlifters. [ABSTRACT FROM AUTHOR]

The stiffness of lower limb joints is a critical characteristic of walking. To investigate the potential of establishing a simple and universal model to describe the characteristics related to vertical vibration during human walking, vertical stiffness is introduced at the knee and hip. A multi-mass-spring model of the human body is established in the vertical direction. In the Fourier form, results of experiments on 14 healthy adults show that the vertical displacements of joints are a function of the leg length and walking cadence, while the ground reaction force is a function of the body weight and walking cadence. The obtained universal equations of vertical displacement and ground reaction force are employed as the input parameters to the proposed multi-mass-spring model. Thus, the vertical stiffness in the knee and hip can then be estimated simultaneously by the subject's weight, leg length, and walking cadence. The variation of vertical stiffness shows different time-varying trends in different gait phases across the entire gait cycle. Finally, the proposed model for vertical stiffness estimation is validated by the vertical oscillation of the pelvis. The average error across three gait cycles for all subjects is 20.48%, with a standard deviation of 5.44%. These results display that the vertical stiffness of knee and hip across the entire gait cycle can be directly estimated by individual parameters that are easy to measure. It provides a different view of human walking analysis and may be applied in future pathological gait recognition, bipedal robots, and lower limb exoskeletons. [ABSTRACT FROM AUTHOR]

This study proposes a force plate with a planar spring and an eddy current displacement sensor to measure the ground reaction force (GRF) of a small insect and reveal its motion characteristics. The proposed force plate comprises a circular aluminum plate, four aluminum springs symmetrically connected to the plate, and an eddy current displacement sensor under the plate. The diameter and thickness of the fabricated plate were 8 and 0.1 mm, respectively. The spring width was 0.4 mm. When a force is applied to the plate, the plate moves vertically downward. Then, an eddy current displacement sensor detects the plate displacement without contact. The applied force can be measured using Hooke's law. The proposed force plate has the advantages of ease of fabrication and cost-effectiveness. The central displacement variation and resonant frequency of the designed springs were evaluated by simulation. Then, we calibrated the fabricated force plate to obtain the sensitivity variation and resonant frequency. The experimental results suggest that the proposed force plate can effectively measure the GRF of a small insect. [ABSTRACT FROM AUTHOR]

Small insects perform agile locomotion, such as running, jumping, and flying. Recently, many robots, inspired by such insect performance, have been developed and are expected to be smaller and more maneuverable than conventional robots. For the development of insect-inspired robots, understanding the mechanical dynamics of the target insect is important. However, evaluating the dynamics via conventional commercialized force sensors is difficult because the exerted force and insect itself are tiny in strength and size. Here, we review force sensor devices, especially fabricated for measuring the tiny forces acting on insects during locomotion. As the force sensor, micro-force plates for measuring the ground reaction force and micro-force probes for measuring the flying force have mainly been developed. In addition, many such sensors have been fabricated via a microelectromechanical system (MEMS) process, due to the process precision and high sensitivity. In this review, we focus on the sensing principle, design guide, fabrication process, and measurement method of each sensor, as well as the technical challenges in each method. Finally, the common process flow of the development of specialized MEMS sensors is briefly discussed. [ABSTRACT FROM AUTHOR]

(1) Background: Significant advances in digital modelling worldwide have been attributed to the practical application of digital musculoskeletal (MS) models in clinical practice. However, the vast majority of MS models are designed to assess adults' mobility, and the range suitable for children is very limited. (2) Methods: Seventeen healthy and 4 cerebral palsy (CP) children were recruited for the gait measurements. Surface electromyography (EMG) and ground reaction forces (GRFs) were acquired simultaneously. The MS model of the adult was adapted to the child and simulated in AnyBody. The differences between measured and MS model-estimated GRFs and muscle activations were evaluated using the following methods: the root-mean-square error (RMSE); the Pearson coefficient r; statistical parametric mapping (SPM) analysis; the coincidence of muscle activity. (3) Results: For muscle activity, the RMSE ranged from 10.4% to 35.3%, the mismatch varied between 16.4% and 30.5%, and the coincidence ranged between 50.7% and 68.4%; the obtained strong or very strong correlations between the measured and model-calculated GRFs, with RMSE values in the y and z axes ranged from 7.1% to 17.5%. (4) Conclusions: Child-adapted MS model calculated muscle activations and GRFs with sufficient accuracy, so it is suitable for practical use in both healthy children and children with limited mobility. [ABSTRACT FROM AUTHOR]

Bipedal models for walkers, originally developed in the research field of biomechanics, have been identified as potential candidates for modelling pedestrians in structural engineering applications. These models provide insight into both the kinetics and kinematics of walking locomotion and are considered to have a significant potential to improve the vibration serviceability assessment of civil engineering structures. Despite this notion, the ability of the bipedal models to represent the key features of the walking gait and natural variability within the pedestrian population are still under-researched. This paper critically evaluates the performance of two bipedal models with rigid legs to realistically both reproduce key features of an individual pedestrian's walking gait and represent a wide range of individuals. The evaluation is performed for walking on a rigid, rather than vibrating, structure due to the availability of experimental data and expectation that successful modelling on rigid surfaces is a necessary condition for progressing towards modelling on the vibrating structures. Ready-to-use equations are provided and the ability of the models to represent the kinematics and kinetics of individual pedestrians as well as the inter-subject variability typical of the human population is critically evaluated. It was found that the two models could generate realistic combinations of the gait parameters and their correlations, but are less successful in reproducing genuine kinetic and kinematics profiles. [ABSTRACT FROM AUTHOR]

Alterations in running shoe design have been studied and used in the prevention of injury and enhancement of performance allowing running shoe companies to market to a variety of runners based on skill level, foot-strike pattern, and even sex. These alterations have been shown to affect biomechanical and physiological variables associated with running. Some shoe companies have designed shoes specifically for biological female runners due to the morphological differences found between male and female feet. The purpose of this study is to determine if sex-specific running shoes can alter female runner biomechanics or physiology. Female runners were asked to run in the male and female models of the Altra Torin 4 Plush shoe to determine if there were differences in ground reaction forces (GRFs), sagittal plane joint angles and moments, oxygen consumption (VO2), respiratory exchange ratio (RER), and perceived level of comfort while running; There were no significant differences in GRFs, sagittal joint angles and moments, VO2, RER, or perceived comfort; There were no differences in measured biomechanical or physiological variables between the female and male version of the shoes suggesting that the alterations made to the female-specific shoe do not provide any additional benefit to female recreational runners. [ABSTRACT FROM AUTHOR]

During locomotion, the foot–ankle system plays an important role for forward progression of the body. The center of pressure (COP) is regarded as the point of the ground reaction force (GRF) vector acting on the foot surface during the stance phase. COP movement trajectory and velocity reflect the stance phase forward progression of the foot segment and the ankle joint motion characteristics. This study aimed to investigate different levels of footwear insole stiffness on COP forward velocity, GRF and ankle joint angles during walking stance phase. Two healthy subjects (one female, one male; age 26.5 ± 6.4 years, height 168.5 ± 2.1 cm, weight 64.9 ± 5.4 kg) participated in this study. Subjects were asked to walk along a 10 m walkway at two different speeds: self–selected normal (SSN) and self–selected fast (SSF). Within each walking speed, subjects were required to walk under two different insole stiffness conditions: (1) normal shoe insole (NSI) from the testing shoe (Nike Free RN Flyknit 2017) used in this study; (2) 1.6 mm thick carbon fiber insole (CFI) fitted within the testing shoe. Stiffer insole (CFI) significantly decreased peak ankle internal rotation angle (p = 0.001) and sagittal plane angle ROM (p = 0.022); additionally, CFI significantly increased peak ankle eversion angle compared to the NSI condition (p = 0.028). In conclusion, increasing footwear insole stiffness would alter stance phase ankle joint motion at SSF walking speed. Additionally, stiffer insoles may tend to decrease COP peak velocity at the initial heel strike and the terminal stance phase. Future research should investigate the combined effects of various insole properties on lower extremity system kinematic and kinetic patterns in various locomotion activities. [ABSTRACT FROM AUTHOR]

The relationship between asymmetry and performance is still undetermined in the literature. Methods of assessing asymmetry have been inconsistent and focused on the analysis of jumping asymmetry. Dual ground reaction forces are prevalent in athlete monitoring, though underutilized in asymmetry research. The purpose of this study was to assess the relationship of countermovement jump (CMJ) impulse asymmetry to performance in collegiate soccer athletes. Male and female athletes were selected from an ongoing athlete research repository database of NCAA D-I soccer athletes. All athletes contributed two maximal effort unweighted (CMJ0) and weighted countermovement jumps (CMJ20) using the mean for calculations. Propulsive phase asymmetry scores (PrPAS) and positive impulse asymmetry scores (PIAS) were calculated to determine the magnitude of asymmetry for each prospective phase. Statistically significant correlations were found between CMJ0 jump height and unweighted PIAS (r = −0.43) in females. Males had statistically significant correlations between CMJ20 jump height and weighted PIAS (r = −0.49). Neither unweighted PrPAS nor weighted PrPAS produced statistically significant correlations (r < 0.26) to their prospective jump heights. When assessing CMJ asymmetry, it is recommended to conduct both weighted and unweighted CMJ testing, utilizing PIAS as the metric to be assessed. [ABSTRACT FROM AUTHOR]

Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals. [ABSTRACT FROM AUTHOR]

Two methods for challenging the musculoskeletal and nervous systems to better exploit the stretch-shortening cycle (SSC) mechanism during plyometric training are reactive strength exercises and accentuated eccentric loading (AEL). Combining repetitive, reactive jumping with AEL poses a novel approach, in which the effects of both methods may be combined to elicit a unique stimulus. This study compared kinematic, kinetic, and electromyographic variables between a control (CON1) and two AEL conditions (AEL2 and AEL3). Additionally, non-reactive and reactive jumps performed within these sets were compared. Participants performed two sets of six countermovement jumps (CMJ) under each loading condition. AEL3 had moderate to large positive effects (es) on peak and mean eccentric force (es = 1.1, 0.8, respectively; both p < 0.01), and eccentric loading rate (es = 0.8, p < 0.01), but no effect on concentric variables or muscle activation intensity. The effects of AEL2 were similar but smaller. With or without AEL, there were moderate to large positive effects associated with reactive CMJ (second jump in a set, compared to the first) on peak and mean eccentric velocity (es = 1.7, 0.8, respectively; both p < 0.01), peak and mean eccentric force (es = 1.3, 1.2, p < 0.01), eccentric loading rate (es = 1.3, p < 0.01) and muscle activity (es = 1.8–1.9, p < 0.01). Concentric variables did not differ. Thus, the flight phase and act of landing during reactive jumps elicited greater increases in eccentric forces, loading rates, and muscle activity than AEL. Nonetheless, kinetic variables were greatest when AEL was combined with reactive jumping. Considering the limitations or complexity associated with most AEL protocols, sets of repetitive (reactive) CMJ may be more pragmatic for augmenting eccentric kinetic variables and neuromuscular stimuli during training. [ABSTRACT FROM AUTHOR]

The human gait can be described as the synergistic activity of all individual components of the sensory–motor system. The central nervous system (CNS) develops synergies to execute endpoint motion by coordinating muscle activity to reflect the global goals of the endpoint trajectory. This paper proposes a new method for assessing temporal dynamic synergies. Principal component analysis (PCA) has been applied on the signals acquired by wearable sensors (inertial measurement units, IMU and ground reaction force sensors, GRF mounted on feet) to detect temporal synergies in the space of two-dimensional PCA cyclograms. The temporal synergy results for different gait speeds in healthy subjects and stroke patients before and after the therapy were compared. The hypothesis of invariant temporal synergies at different gait velocities was statistically confirmed, without the need to record and analyze muscle activity. A significant difference in temporal synergies was noticed in hemiplegic gait compared to healthy gait. Finally, the proposed PCA-based cyclogram method provided the therapy follow-up information about paretic leg gait in stroke patients that was not available by observing conventional parameters, such as temporal and symmetry gait measures. [ABSTRACT FROM AUTHOR]

Pressure-detecting insoles such as the Insole3 have potential as a portable alternative for assessing vertical ground reaction force (vGRF) outside of specialized laboratories. This study evaluated whether the Insole3 is a valid and reliable alternative to force plates for measuring vGRF. Eleven healthy participants walked overground at slow and moderately paced speeds and ran at a moderate pace while collecting vGRF simultaneously from a force plate (3000 Hz) and Insole3 (100 Hz). Intraclass correlation coefficients (ICC) demonstrated excellent vGRF agreement between systems during both walking speeds for Peak 1, Peak 2, the valley between peaks, and the vGRF impulse (ICC > 0.941). There was excellent agreement during running for the single vGRF peak (ICC = 0.942) and impulse (ICC = 0.940). The insoles slightly underestimated vGRF peaks (−3.7% to 0.9% bias) and valleys (−2.2% to −1.8% bias), and slightly overestimated impulses (4.2% to 5.6% bias). Reliability between visits for all three activities was excellent (ICC > 0.970). The Insole3 is a valid and reliable alternative to traditional force plates for assessing vGRF during walking and running in healthy adults. The excellent ICC values during slow walking suggests that the Insole3 may be particularly suitable for older adults in clinical and home settings. [ABSTRACT FROM AUTHOR]

Force plate assessments, such as countermovement jumps and isometric mid-thigh pulls, examine performances (e.g., jump height, force, power) and movement strategies (e.g., asymmetries, durations), and are best suited to characterize and monitor physical capabilities, not predict injuries. To begin applying force plate technologies, users must first; (1) develop a data management plan to visualize and capture data over time; (2) select appropriate force plates for their scenario; (3) design appropriate testing protocols to ensure valid and reliable data. Force plate assessments may be added to existing testing, serve as separate testing batteries for annual profile testing to compare individuals and understand initial physical capabilities, or for more frequent testing (i.e., monthly or weekly) to monitor training-related adaptations or neuromuscular fatigue. Although these assessments inform evidence-based program designs, human performance practitioners must understand the considerations for conducting appropriate force plate testing, as well as proper visualizations and management of force plate data. Thus, the aim of this review is to provide evidence-based practices for utilizing force plates in tactical populations (e.g., military, firefighters, police). This includes best practices to implement testing for performance profiling, training adaptations, and monitoring neuromuscular fatigue and force asymmetries. Of note, due to the large amount of force-time metrics to choose from, this article provides general examples of important metrics to monitor and training recommendations based on changes to these force-time metrics, followed by specific examples in three case studies. [ABSTRACT FROM AUTHOR]

Background and Objectives: Medial knee osteoarthritis is known to increase the mechanical load on the medial compartment of the knee joint during walking; however, it is not visually understood how much the mechanical load increases nor where in the medial compartment of the knee joint that load is focused. Therefore, we conducted a simulation study to determine the location and amount of the mechanical load in the medial compartment of the knee joint during the stance phase. Materials and Methods: Subject was a patient with right medial knee osteoarthritis. Computed tomography imaging and gait analysis were performed on subject. The CT image of the right knee was calculated using finite element analysis software. Since this software can set the flexion angle arbitrarily while maintaining the nonuniform material properties of the bone region, the model is constructed by matching the knee joint extension image obtained by CT to the loading response phase of gait analysis. The data of muscle exertion tension and vertical ground reaction force were inserted into the knee joint model created from the computed tomography-based finite element method, and the knee joint compressive stress was calculated. Results: With regard to compressive stress, the tibia showed high stress at 4.10 to 5.36 N/mm². The femur showed high stress at 4.00 to 6.48 N/mm2. The joint compressive stress on the medial compartment of the knee joint was found to concentrate on the edge of the medial tibial condyle in the medial knee osteoarthritis subject. Conclusions: The measurement method of knee joint compressive stress by computed tomography-based finite element method can visually be a reliable method of measuring joint compressive stress in the medial knee osteoarthritis. This reflects the clinical findings because concentration of stress on the medial knee joint was observed at the medial osteophyte. [ABSTRACT FROM AUTHOR]

Athletic performance, technique assessment, and injury prevention are all important aspects in sports for both professional and amateur athletes. Wearable technology is attracting the research community's interest because of its capability to provide real-time biofeedback to coaches and athletes when on the field and outside of more restrictive laboratory conditions. In this paper, a novel wearable motion sensor-based system has been designed and developed for athletic performance assessment during running and jumping tasks. The system consists of a number of components involving embedded systems (hardware and software), back-end analytics, information and communications technology (ICT) platforms, and a graphical user interface for data visualization by the coach. The system is able to provide automatic activity recognition, estimation of running and jumping metrics, as well as vertical ground reaction force (GRF) predictions, with sufficient accuracy to provide valuable information as regards training outcomes. The developed system is low-power, sufficiently small for real-world scenarios, easy to use, and achieves the specified communication range. The system's high sampling rate, levels of accuracy and performance enables it as a performance evaluation tool able to support coaches and athletes in their real-world practice. [ABSTRACT FROM AUTHOR]

Vertical ground reaction force (vGRF) can be measured by force plates or instrumented treadmills, but their application is limited to indoor environments. Insoles remove this restriction but suffer from low durability (several hundred hours). Therefore, interest in the indirect estimation of vGRF using inertial measurement units and machine learning techniques has increased. This paper presents a methodology for indirectly estimating vGRF and other features used in gait analysis from measurements of a wearable GPS-aided inertial navigation system (INS/GPS) device. A set of 27 features was extracted from the INS/GPS data. Feature analysis showed that six of these features suffice to provide precise estimates of 11 different gait parameters. Bagged ensembles of regression trees were then trained and used for predicting gait parameters for a dataset from the test subject from whom the training data were collected and for a dataset from a subject for whom no training data were available. The prediction accuracies for the latter were significantly worse than for the first subject but still sufficiently good. K-nearest neighbor (KNN) and long short-term memory (LSTM) neural networks were then used for predicting vGRF and ground contact times. The KNN yielded a lower normalized root mean square error than the neural network for vGRF predictions but cannot detect new patterns in force curves. [ABSTRACT FROM AUTHOR]

The center of pressure (COP), which is defined as the point at which the resultant ground reaction force (GRF) is applied on a body, provides valuable information for postural stability assessment. This is because the fundamental goal of balance control is to regulate the center of mass (COM) of the human body by adaptively changing the position of the COP. By using Newtonian mechanics to develop two equations that relate the two-dimensional COP coordinates to the GRF components, one can easily determine the location of the COP using a force plate. An important property of these two equations is that for a given COP position, there exists an infinite number of GRF component combinations that can satisfy these two equations. However, the manner in which a postural control system deals with such redundancy is still unclear. To address this redundancy problem, we introduce four postural stability features by quantifying the coupling strengths between the COP coordinates and their GRF components. Experiments involving younger (18–24 years old) and older (65–73 years) participants were conducted. The efficacy of the proposed features was demonstrated by comparing the differences between variants of each feature for each age group (18–24 and 65–73 years). The results demonstrated that the coupling strengths between the anterior–posterior (AP) direction coordinate of the COP and its GRF components for the older group were significantly higher than those of the younger group. These experimental results suggest that (1) the balance control system of the older group is more constrained than that of the younger group in coordinating the GRF components and (2) the proposed features are more sensitive to age variations than one of the most reliable and accurate conventional COP features. The best testing classification accuracy achieved by the proposed features was 0.883, whereas the testing classification accuracy achieved by the most accurate conventional COP feature was 0.777. Finally, by investigating the interactions between the COP and its GRF components using the proposed features, we found that that the AP component of the GRF of younger people plays a more active role in balance control than that of the GRF of older people. Based on these findings, it is believed that the proposed features can be used as a set of stability measures to assess the effects on posture stability from various health-related conditions such as aging and fall risk. [ABSTRACT FROM AUTHOR]

This study investigated how midsole hardness of basketball footwear affects lower extremity biomechanics and impacts perception in drop vertical jumps. Eighteen male basketball players performed drop vertical jumps from three heights (31 cm, 46 cm, 61 cm) in basketball shoes of different midsole hardness (50, 60 Asker C). Biomechanical variables of the lower extremity and subjective perception were measured. This study found a significant drop height effect on the lower extremity biomechanics (p < 0.05), with greater ground reaction forces, joint kinetics, and prelanding muscle activation levels observed at higher drop heights. Basketball shoes with a softer midsole led to higher forefoot peak force (p = 0.028) amid lower rearfoot peak force (p = 0.046), lower peak flexion moments at the ankle (p = 0.024) and hip joints (p = 0.029), and greater prelanding muscle activation in the rectus femoris (p = 0.042) and tibialis anterior (p = 0.043). It is concluded that changing midsole hardness within a commercially relevant range triggered a different prelanding muscle activation strategy and hence altered the magnitudes of ground reaction forces and joint loadings during landing. Subjectively, participants perceived higher landing impacts with greater drop heights, though the strength of the associations were weak. [ABSTRACT FROM AUTHOR]

Automatic terrain classification in lower limb rehabilitation systems has gained worldwide attention. In this field, a simple system architecture and high classification accuracy are two desired attributes. In this article, a smart neuromuscular–mechanical fusion and machine learning-based terrain classification technique utilizing only two electromyography (EMG) sensors and two ground reaction force (GRF) sensors is reported for classifying three different terrains (downhill, level, and uphill). The EMG and GRF signals from ten healthy subjects were collected, preprocessed and segmented to obtain the EMG and GRF profiles in each stride, based on which twenty-one statistical features, including 9 GRF features and 12 EMG features, were extracted. A support vector machine (SVM) machine learning model is established and trained by the extracted EMG features, GRF features and the fusion of them, respectively. Several methods or statistical metrics were used to evaluate the goodness of the proposed technique, including a paired-t-test and Kruskal–Wallis test for correlation analysis of the selected features and ten-fold cross-validation accuracy, confusion matrix, sensitivity and specificity for the performance of the SVM model. The results show that the extracted features are highly correlated with the terrain changes and the fusion of the EMG and GRF features produces the highest accuracy of 96.8%. The presented technique allows simple system construction to achieve the precise detection of outcomes, potentially advancing the development of terrain classification techniques for rehabilitation. [ABSTRACT FROM AUTHOR]

The NintendoWii Balance Board (WBB) has been suggested as an inexpensive, portable and accessible alternative to costly laboratory-grade force plates for measuring the vertical ground reaction force (vGRF) and center of pressure (COP). Kinetic gait analysis provides important information for the rehabilitation of patients with gait disorders; however, the validity of the WBB for measuring kinetic gait parameters has not been evaluated. Therefore, the purpose of this study is to determine the accuracy of walking force measurements—which change dynamically in a short period of stance time—collected with the WBB. Three healthy adults were asked to walk 10 steps along both straight and curved paths in clockwise (CW) and counterclockwise (CCW) directions while measurements were taken using the WBB and the force plate. The accuracy of the vGRF, COP trajectory, and stance duration were evaluated using the root-mean-square error (RMSE), Pearson's correlation coefficient and Bland–Altman plots (BAPs) to compare the WBB and the force plate. The results of the vGRF showed high accuracy (r > 0.96 and %RMSE < 6.1% in the mean values), and the stance duration as defined by the vGRF and COP trajectory was equivalent to that of commercial instrumented insoles, which are used as an alternative to the force plates. From these results, we determined that the WBB may be used for kinetic gait analysis in clinical settings where lower accuracy is acceptable. [ABSTRACT FROM AUTHOR]

Joint moment estimation by a camera-based motion measurement system and a force plate has a limitation of measurement environment and is costly. The purpose of this paper is to evaluate quantitatively inertial sensor-based joint moment estimation methods with five-link, four-link and three-link rigid body models using different trunk segmented models. Joint moments, ground reaction forces (GRF) and center of pressure (CoP) were estimated for squat and sit-to-stand movements in the sagittal plane measured with six healthy subjects. The five-link model and the four-link model that the trunk was divided at the highest point of the iliac crest (four-link-IC model) were appropriate for joint moment estimation with inertial sensors, which showed average RMS values of about 0.1 Nm/kg for all lower limb joints and average correlation coefficients of about 0.98 for hip and knee joints and about 0.80 for ankle joint. Average root mean square (RMS) errors of horizontal and vertical GRFs and CoP were about 10 N, 15 N and 2 cm, respectively. Inertial sensor-based method was suggested to be an option for estimating joint moments of the trunk segments. Inertial sensors were also shown to be useful for the bottom-up estimation method using measured GRFs, in which average RMS values and average correlation coefficients were about 0.06 Nm/kg and larger than about 0.98 for all joints. [ABSTRACT FROM AUTHOR]

Inertial measurement units (IMUs) have shown promising outcomes for estimating gait event detection (GED) and ground reaction force (GRF). This study aims to determine the best sensor location for GED and GRF prediction in gait using data from IMUs for healthy and medial knee osteoarthritis (MKOA) individuals. In this study, 27 healthy and 18 MKOA individuals participated. Participants walked at different speeds on an instrumented treadmill. Five synchronized IMUs (Physilog ® , 200 Hz) were placed on the lower limb (top of the shoe, heel, above medial malleolus, middle and front of tibia, and on medial of shank close to knee joint). To predict GRF and GED, an artificial neural network known as reservoir computing was trained using combinations of acceleration signals retrieved from each IMU. For GRF prediction, the best sensor location was top of the shoe for 72.2% and 41.7% of individuals in the healthy and MKOA populations, respectively, based on the minimum value of the mean absolute error (MAE). For GED, the minimum MAE value for both groups was for middle and front of tibia, then top of the shoe. This study demonstrates that top of the shoe is the best sensor location for GED and GRF prediction.

The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population. [ABSTRACT FROM AUTHOR]