Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system

Background Foot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was to propose a deep learning model that could predict pressure distribution of the whole foot based on information obtained from a small number of pressure sensors in an insole. Methods Twenty young and twenty older adults walked a straight pathway at a preferred speed with a Pedar-X system in anti-skid socks. A long short-term memory (LSTM) model was used to predict foot pressure distribution. Pressure values of nine major sensors and the remaining 90 sensors in a Pedar-X system were used as input and output for the model, respectively. The performance of the proposed LSTM structure was compared with that of a traditionally used adaptive neuro-fuzzy interference system (ANFIS). A low-cost insole system consisting of a small number of pressure sensors was fabricated. A gait experiment was additionally performed with five young and five older adults, excluding subjects who were used to construct models. The Pedar-X system placed parallelly on top of the insole prototype developed in this study was in anti-skid socks. Sensor values from a low-cost insole prototype were used as input of the LSTM model. The accuracy of the model was evaluated by applying a leave-one-out cross-validation. Results Correlation coefficient and relative root mean square error (RMSE) of the LSTM model were 0.98 (0.92 ~ 0.99) and 7.9 ± 2.3%, respectively, higher than those of the ANFIS model. Additionally, the usefulness of the proposed LSTM model for fabricating a low-cost insole prototype with a small number of sensors was confirmed, showing a correlation coefficient of 0.63 to 0.97 and a relative RMSE of 12.7 ± 7.4%. Conclusions This model can be used as an algorithm to develop a low-cost portable smart insole system to monitor age-related physiological and anatomical alterations in foot. This model has the potential to evaluate clinical rehabilitation status of patients with pathological gait, falling, and various foot pathologies when more data of patients with various diseases are accumulated for training.

TOPOLOGY OPTIMIZATION OF LIGHTWEIGHT STRUCTURES WITH APPLICATION TO BONE SCAFFOLDS AND 3D PRINTED SHOES FOR DIABETICS

An automatic complex topology lightweight structure generation method (ACTLSGM) is presented to automatically generate 3D models of lightweight truss structures with a boundary surface of any shape. The core idea of the ACTLSGM is to use the PIMesh, a mesh generation algorithm developed by the authors, to generate node distributions inside the object representing the boundary surface of the target complex topology structures; raw lightweight truss structures are then generated based on the node distributions; the resulting lightweight truss structure is then created by adjusting the radius of the raw truss structures using an optimization algorithm based on finite element truss analysis. The finite element analysis-based optimization algorithm can ensure the resulting structures satisfy the design requirements on stress distributions or stiffness. Three demos, including a lightweight structure for a cantilever beam, a femur bone scaffold, and a 3D shoe sole model with adaptive stiffness that can be used to adjust foot pressure distributions for patients with diabetic foot problems, are generated to demonstrate the performance of the ACTLSGM. The ACTLSGM is not limited to generating 3D models of medical devices, but can be applied in many other fields, including 3D printing infills and other fields where customized lightweight structures are required.

Foot Insole Pressure Distribution during the Golf Swing in Professionals and Amateur Players

There are numerous articles that study the ground reaction forces during the golf swing, among which only a few analyze the pressure pattern distributed on the entire surface of the foot. The current study compares the pressure patterns on the foot insoles of fifty-five golfers, from three different performance levels, playing swings with driver and 5-iron clubs in the driving range. Five swings were selected for each club. During each swing, ultra-thin insole sensors (4 sensors/cm^2) measure foot pressure at the frequency of 100 Hz. To perform statistical analysis, insole sensors are clustered to form seven areas, with the normalized pressure of each area being our dependent variable. A video camera was used to label the five key instants of the swing. Statistical analysis demonstrates a significant difference between the pressure distribution pattern of the left and right feet for both driver and 5-iron. However, the pressure distribution pattern remains almost the same when switching the club type from 5-iron to driver. We have also observed that there are significant differences between the pattern of professionals and players with medium and high handicap. The obtained pattern agrees with the principle of weight transfer with a different behavior between the medial and lateral areas of the foot.

Human-In-The-Loop Optimization of A Wearable Robot Using Foot Pressure Sensors

Background: Individuals with below-knee amputation (BKA) experience increased physical effort when walking, and the use of a robotic ankle-foot prosthesis (AFP) can reduce such effort. Our prior study on a robotic AFP showed that walking effort could be reduced if the robot is personalized to the wearer. The personalization is accomplished using human-in-the-loop (HIL) optimization, in which the cost function is based on a real-time physiological signal indicating physical effort. The conventional physiological measurement, however, requires a long estimation time, hampering real-time optimization due to the limited experimental time budget. In addition, the physiological sensor, based on respiration uses a mask with rigid elements that may be difficult for the wearer to use. Prior studies suggest that a symmetry measure using a less intrusive sensor, namely foot pressure, could serve as a metric of gait performance. This study hypothesized that a function of foot pressure, the symmetric foot force-time integral, could be used as a cost function to rapidly estimate the physical effort of walking; therefore, it can be used to personalize assistance provided by a robotic ankle in a HIL optimization scheme. Methods: We developed a new cost function derived from a well-known clinical measure, the symmetry index, by hypothesizing that foot force-time integral (FFTI) symmetry would be highly correlated with metabolic cost. We conducted experiments on human participants (N = 8) with simulated amputation to test the new cost function. The study consisted of a discrete trial day, an HIL optimization training day, and an HIL optimization data collection day. We used the discrete trial day to evaluate the correlation between metabolic cost and a cost function using symmetric FFTI percentage. During walking, we varied the prosthetic ankle stiffness while measuring foot pressure and metabolic rate. On the second and third days, HIL optimization was used to find the optimal stiffness parameter with the new cost function using symmetric FFTI percentage. Once the optimal stiffness parameter was found, we validated the performance with comparison to a weight-based stiffness and control-off conditions. We measured symmetric FFTI percentage during the stance phase, prosthesis push-off work, metabolic cost, and user comfort in each condition. We expected the optimized prosthetic ankle stiffness based on the newly developed cost function could reduce the energy expenditure during walking for the individuals with simulated amputation. Results: We found that the cost function using symmetric foot force-time integral percentage presents a reasonable correlation with measured metabolic cost (Pearson’s R > 0.62). When we employed the new cost function in HIL ankle-foot prosthesis parameter optimization, 8 individuals with simulated amputation reduced their cost of walking by 15.9% (p = 0.01) and 16.1% (p = 0.02) compared to the weight-based and control-off conditions, respectively. The symmetric FFTI percentage for the optimal condition tended to be closer to the ideal symmetry value (50%) compared to weight-based (p = 0.23) and control-off conditions (p = 0.04). Conclusion: This study suggests that foot force-time integral symmetry using foot pressure sensors can be used as a cost function when optimizing a wearable robot parameter.

Static and Dynamic Plantar Pressure and Normalized Plantar Pressure Characteristics among Children, Adults, and the Elderly

Background: A better understanding of plantar pressure while standing and walking would help in improving balance and gait performance across different age ranges. Objective: To clarify the differences of plantar pressure while standing and walking among children, adults, and the elderly. Materials and Methods: Fifty-three participants including eleven aged 3 to 8 years, thirty aged 20 to 40 years, and twelve aged 60 to 90 years were included in the present study. Plantar pressure and related parameters while quiet standing and walking with self-selected speed were assessed. Results: In static plantar pressure, no significant differences were observed of mean different pressure and mean different contact area between dominant and non-dominant limbs among the three groups, while center of pressure (COP) displacement was shown as significantly greater between children and adults (p<0.05). For dynamic plantar pressure, no significant differences in COP velocity were found among the three groups. The elderly showed significant lower normalized maximum plantar pressure in areas of the second and third metatarsal, and internal heel compared with the young adults (p<0.05). Additionally, normalized maximum plantar pressures among children seemed to differ from adults. Conclusion: Plantar pressure characteristics could indicate that children develop gait ability in braking and propulsion phases with greater heel and toe function, while the ability of braking and propulsion declined with aging. These could reflect balance ability while standing or walking. Keywords: Foot pressure; Children; Elderly; Normalization

Effects of obesity and foot arch height on gait mechanics: A cross-sectional study

Foot arch structure contributes to lower-limb joint mechanics and gait in adults with obesity. However, it is not well-known if excessive weight and arch height together affect gait mechanics compared to the effects of excessive weight and arch height alone. The purpose of this study was to determine the influences of arch height and obesity on gait mechanics in adults. In this study, 1) dynamic plantar pressure, 2) spatiotemporal gait parameters, 3) foot progression angle, and 4) ankle and knee joint angles and moments were collected in adults with normal weight with normal arch heights (n = 11), normal weight with lower arch heights (n = 10), obesity with normal arch heights (n = 8), and obesity with lower arch heights (n = 18) as they walked at their preferred speed and at a pedestrian standard walking speed, 1.25 m/s. Digital foot pressure data were used to compute a measure of arch height, the Chippaux-Smirak Index (CSI). Our results revealed that BMI and arch height were each associated with particular measures of ankle and knee joint mechanics during walking in healthy young adults: (i) a higher BMI with greater peak internal ankle plantar-flexion moment and (ii) a lower arch height with greater peak internal ankle eversion and abduction moments and peak internal knee abduction moment (i.e., external knee adduction moment). Our results have implications for understanding the role of arch height in reducing musculoskeletal injury risks, improving gait, and increasing physical activity for people living with obesity.

GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network

Gait walking patterns are one of the key research topics in natural biometrics. The temporal information of the unique gait sequence of a person is preserved and used as a powerful data for access. Often there is a dive into the flexibility of gait sequence due to unstructured and unnecessary sequences that tail off the necessary sequence constraints. The authors in this work present a novel perspective, which extracts useful gait parameters regarded as independent frames and patterns. These patterns and parameters mark as unique signature for each subject in access authentication. This information extracted learns to identify the patterns associated to form a unique gait signature for each person based on their style, foot pressure, angle of walking, angle of bending, acceleration of walk, and step-by-step distance. These parameters form a unique pattern to plot under unique identity for access authorization. This sanitized data of patterns is further passed to a residual deep convolution network that automatically extracts the hierarchical features of gait pattern signatures. The end layer comprises of a Softmax classifier to classify the final prediction of the subject identity. This state-of-the-art work creates a gait-based access authentication that can be used in highly secured premises. This work was specially designed for Defence Department premises authentication. The authors have achieved an accuracy of 90 % ± 1.3 % in real time. This paper mainly focuses on the assessment of the crucial features of gait patterns and analysis of gait patterns research.