Optimal degrees of freedom of the lower extremities for human walking and running.

Determining the degrees of freedom (DOF) of the linked rigid-body model, representing a multi-body motion of the human lower extremity, is one of the most important procedures in locomotion analysis. However, a trade-off exists between the quality of data fitting and the generalizability of the model. This study aimed to determine the optimal DOF of the model for the lower extremities that balance the goodness-of-fit and generalizability of the model during walking and running using Akaike's information criterion (AIC). Empirically obtained kinematic data for the lower extremities during walking and running were fitted by models with 9, 18, or 22 DOF. The relative quality of these models was assessed using their bias-corrected AIC (cAIC) value. A significant simple main effect of the model was found on the cAIC value for both walking and running conditions. Pairwise comparisons revealed that the cAIC value of the 18-DOF model was significantly smaller than that of the 9-DOF (walking: p < 0.001, running: p = 0.010) and 22-DOF (walking: p < 0.001, running: p < 0.001) models. These findings suggest that the 18-DOF model is optimal for representing the lower extremities during walking and running, in terms of goodness-of-fit and generalizability.

Identification of characteristics of foot position and angle during swing phase in fallers using principal component analysis.

Identifying the characteristics of fallers is important for preventing falls because such events may reduce quality of life. It has been reported that several variables related to foot positions and angles during gait (e.g., sagittal foot angle and minimum toe clearance) differ between fallers and non-fallers. However, examining such representative discrete variables may not be sufficient to detect crucial information, which may be contained in the large portions of unanalyzed data. Therefore, we aimed to identify the comprehensive characteristics of foot position and angle during the swing phase of gait in non-fallers and fallers using principal component analysis (PCA). Thirty non-fallers and 30 fallers were recruited for this study. We performed PCA to reduce the dimensions of foot positions and angles during the swing phase and obtained principal component scores (PCSs) for each principal component vector (PCV), which were then compared between groups. The results revealed that the PCS of PCV3 in fallers was significantly larger than that in non-fallers (p = 0.003, Cohen's d = 0.80). We reconstructed waveforms of foot positions and angles during the swing phase using PCV3 and our main findings can be summarized as follows. Compared to non-fallers, fallers have a 1) low average foot position in the z-axis (i.e., height) during the initial swing phase 2) small average foot angle in the x-axis (i.e., rotation in the sagittal plane), during the initial swing phase, and 3) large variability in foot position in the y-axis (i.e., anterior/posterior position) during the initial swing phase. We can conclude that these are characteristics of gait related to fallers. Therefore, our findings may be beneficial for evaluating fall risk during gait using a device such as a shoe- or insole-embedded inertial measurement unit.

Development of a data platform for monitoring personal health records in Japan: The Sustaining Health by Integrating Next-generation Ecosystems (SHINE) Study.

BACKGROUND: The Sustaining Health by Integrating Next-generation Ecosystems (SHINE) Study was developed as a data platform that incorporates personal health records (PHRs) into health-related data at the municipal level in Japan. This platform allows analyses of the associations between PHRs and future health statuses, and supports the production of evidence for developing preventive care interventions. Herein, we introduce the SHINE Study's profile and describe its use in preliminary analyses. METHODS: The SHINE Study involves the collection of participants' health measurements and their addition to various health-related data from the Longevity Improvement & Fair Evidence (LIFE) Study. With cooperation from municipal governments, measurements can be acquired from persons enrolled in government-led long-term care prevention classes and health checkups who consent to participate in the SHINE Study. For preliminary analyses, we collected salivary test measurements, lifelog measurements, and gait measurements; these were linked with the LIFE Study's database. We analyzed the correlations between these measurements and the previous year's health care expenditures. RESULTS: We successfully linked PHR data of 33 participants for salivary test measurements, 44 participants for lifelog measurements, and 32 participants for gait measurements. Only mean torso speed in the gait measurements was significantly correlated with health care expenditures (r = -0.387, P = 0.029). CONCLUSION: The SHINE Study was developed as a data platform to collect and link PHRs with the LIFE Study's database. The analyses undertaken with this platform are expected to contribute to the development of preventive care tools and promote health in Japan.

Determination of the optimal number of linked rigid-bodies of the trunk during walking and running based on Akaike's information criterion.

BACKGROUND: In the three-dimensional kinematic analysis of the trunk during human locomotion, a multi-segmental rigid-body model would be a better representation for the trunk compared with a single rigid-body model with regard to goodness-of-fit. However, there is a trade-off between data fitting and the simplicity of the model. RESEARCH QUESTION: This study aimed to determine the optimal number of rigid-body segments during walking and running using Akaike's information criterion (AIC), which determines the model that has goodness-of-fit and is generalizable. METHODS: Empirically obtained kinematic data for the trunk during walking and running were fitted by one-, two-, three-, and six-linked rigid-body models using a nonlinear optimization algorithm. The relative quality of these models was assessed using their bias-corrected AIC (AICc) value. RESULTS: The AICc values of two- and three-linked rigid-body models were significantly smaller than those of one- or six-segment models for the walking trial. For the running trial, the AICc values of two-, three-, and six-segment models were significantly smaller than that of the single rigid-body model. DISCUSSION: These results suggest that both two- and three-linked rigid-body models would be better than the one- and six-linked rigid-body representations for analyzing trunk movement during walking, whereas the two-, three-, and six-linked models would be comparably well-balanced models in terms of both the goodness-of-fit and generalizability for running analysis.

Quantitative evaluation of linked rigid-body representations of the trunk.

BACKGROUND: The trunk is often simplified as a small number of rigid-body segments to reduce the complexity of its multi-segmental structure. However, such rigid-body representations of the trunk may overlook its flexible movement owing to its multi-segmental structure. RESEARCH QUESTION: The purpose of this study is to quantitatively assess the effects of the deformability on the resultant trunk kinematics when the trunk is modeled with numerous rigid-body segments. METHODS: Three-dimensional kinematic data of 10 male subjects were obtained during static and dynamic trials. The trunk in both static and dynamic trials was modeled as a single rigid-body segment or as two, three, or six linked rigid-body segments, and a non-linear optimization analysis was performed to minimize the difference between the actual and modeled position data. Position errors were evaluated to assess the difference in three-dimensional positions between the actual and modeled data for each model. The total angular displacement was evaluated to examine to what extent each model describes the actual multi-segmental trunk movement. RESULTS: The position error between the modeled and actual kinematic data of the trunk was up to 12 mm and 11 mm when the trunk was simplified as one segment, but the error decreased to 5 mm and 7 mm when the trunk was modeled with six segments during the static and dynamic trials, respectively. The total angular displacement increased as the number of rigid-body segments increased during both trials. SIGNIFICANCE: These results imply that a small number of linked rigid-body representations underestimates the actual multi-segmental trunk movement during dynamic movement. These findings are useful in determining the optimal number of rigid-body segments for analysis of the trunk.

Quantitative assessment of trunk deformation during running.

The trunk has a multi-segmental structure and is composed of the cervical, thoracic, and lumber spines and surrounding soft tissue elements; this allows flexible deformation during dynamic movements. The purpose of this study was to quantitatively assess trunk deformation during dynamic movement. Ten male subjects performed running at four different speeds: 8km/h, 10km/h, 12km/h, and 14km/h. Forty reflective markers were placed on the backs of these individuals to define 56 small triangular areas, and three-dimensional kinematic data was recorded with a motion capture system. The coefficients of variation (CV) of the horizontal and vertical lengths between two adjacent markers and the standard deviation (SD) of the normal vectors of triangular areas were calculated as measures for translational and angular trunk deformation, respectively. Up to about 14% of CV and 78° of SD appeared as the measure of translational and angular deformation, respectively. These results imply that the trunk underwent a significant amount of position-specific deformation. These findings would be useful in the construction of an optimal trunk segment model to represent the complex and flexible trunk movement during dynamic movements.

The knee extensor moment arm is associated with performance in male sprinters.

PURPOSE: Although large knee extensor torque contributes to superior sprint performance, previous findings have indicated that the quadriceps cross-sectional area (CSA), a pivotal morphological regulator of knee extensor torque, is not correlated with performance in sprinters. We hypothesized that the knee extensor moment arm (MA), another main morphological regulator of knee extensor torque, may affect sprint performance. To test this hypothesis, we examined the relationship between knee extensor MA and sprint performance. METHODS: The quadriceps CSA and knee extensor MA in 32 well-trained male sprinters and 32 male non-sprinters were measured using magnetic resonance imaging. RESULTS: Knee extensor MA, but not quadriceps CSA, was greater in sprinters than in non-sprinters (P = 0.013). Moreover, knee extensor MA, but not the quadriceps CSA, was correlated with the personal best time in a 100-m race in sprinters (r = -0.614, P < 0.001). Furthermore, among 24 sprinters who participated in the 60-m sprint test, knee extensor MA was correlated with sprinting velocities in the acceleration (r = 0.717, P < 0.001) and maximum speed (r = 0.697, P < 0.001) phases. CONCLUSION: The present study demonstrates that the knee extensor MA is greater in sprinters than in non-sprinters, and this morphological structure in sprinters is associated with sprint performance. Therefore, for the first time, we provided evidence that a greater knee extensor MA in sprinters may be an advantageous for achieving superior sprint performance.

An image-processing based technique to obtain instantaneous horizontal walking and running speed.

Walking and running speed is a fundamental parameter studied in a wide range of areas such as sport biomechanics, rehabilitation, health promotion of the elderly, etc. Given that walking or running speed is not constant even within a stride, instantaneous changes in the body motion need to be evaluated to better understand one's performance. In this study, a new cost- and time- efficient methodology to determine instantaneous horizontal walking and running speed was developed. The newly developed method processes the movies taken with a (high-speed) camera. It consists of five sub-steps, which are performed in a serial order: (1) Subtraction of the background image, (2) filtering, (3) binarization and centroid determination, (4) transformation to the laboratory coordinate system and (5) differentiation. To test the accuracy of the newly developed method, the output (position and speed) was compared with the data obtained using motion capture. The average root mean squared (RMS) error (difference between the outputs of the newly developed method and motion capture) of position-time curves was 0.011m-0.033m. The average RMS error of speed-time curves was 0.054m/s-0.076m/s. It was shown that this new method produces accurate outputs of instantaneous walking and running speed.