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This dataset presents human foot joints kinematics and kinetics data during walking, classified by static foot posture, filling a gap in existing lower limb databases that lack data on foot joints beyond the ankle or on static posture data, despite its link to foot and lower limb pathologies. Kinematics were recorded using a three-dimensional mocap system, and kinetics through a pressure platform, employing a multi-segment foot model including the ankle, midtarsal and first metatarsophalangeal joint. The dataset contains 350 recordings of right foot joint angles and moments and contact pressures from 70 healthy subjects with varying static posture (highly pronated, highly supinated and normal). Data were collected at 100 Hz, filtered and resampled to 100 frames throughout the stance phase. Descriptive data are also provided: age, weight, height, BMI and foot anthropometric data and foot posture index. Plots, tables and ANOVAs are included for validation. Presented in.xlsx and.mat formats, this database can assist professionals in corrective footwear design, insole customization, surgical planning, and evaluating interventions on foot dynamics. [ABSTRACT FROM AUTHOR]

When studying hand kinematics, it is key to differentiate between free motion and manipulation. This differentiation can be achieved using pressure sensors or through visual analysis in the absence of sensors. Certain data gloves, such as the CyberGlove II, allow recording hand kinematics with good accuracy when properly calibrated. Other gloves, such as the Virtual Motion Glove 30 (VMG30), are also equipped with pressure sensors to detect object contact. The aim of this study is to perform a technical validation to evaluate the feasibility of using virtual reality gloves with pressure sensors such as the VMG30 for hand kinematics characterization during product manipulation, testing its accuracy for motion recording when compared with CyberGlove as well as its ability to differentiate between free motion and manipulation using its pressure sensors in comparison to visual analysis. Firstly, both data gloves were calibrated using a specific protocol developed by the research group. Then, the active ranges of motion of 16 hand joints angles were recorded in three participants using both gloves and compared using repeated measures ANOVAs. The detection capability of pressure sensors was compared to visual analysis in two participants while performing six tasks involving product manipulation. The results revealed that kinematic data recordings from the VMG30 were less accurate than those from the CyberGlove. Furthermore, the pressure sensors did not provide additional precision with respect to the visual analysis technique. In fact, several pressure sensors were rarely activated, and the distribution of pressure sensors within the glove was questioned. Current available gloves such as the VMG30 would require design improvements to fit the requirements for kinematics characterization during product manipulation. The pressure sensors should have higher sensitivity, the pressure sensor's location should comprise the palm, glove fit should be improved, and its overall stiffness should be reduced. [ABSTRACT FROM AUTHOR]

The recording of hand kinematics during product manipulation is challenging, and certain degrees of freedom such as distal interphalangeal (DIP) joints are difficult to record owing to limitations of the motion capture systems used. DIP joint kinematics could be estimated by taking advantage of its kinematic linkage with proximal interphalangeal (PIP) and metacarpophalangeal joints. This work analyses this linkage both in free motion conditions and during the performance of 26 activities of daily living. We have studied the appropriateness of different types of linear regressions (several combinations of independent variables and constant coefficients) and sets of data (free motion and manipulation data) to obtain equations to estimate DIP joints kinematics both in free motion and manipulation conditions. Errors that arise when estimating DIP joint angles assuming linear relationships using the equations obtained both from free motion data and from manipulation data are compared for each activity of daily living performed. Estimation using manipulation condition equations implies a lower mean absolute error per task (from 5.87° to 13.67°) than using the free motion ones (from 9° to 17.87°), but it fails to provide accurate estimations when passive extension of DIP joints occurs while PIP is flexed. This work provides evidence showing that estimating DIP joint angles is only recommended when studying free motion or grasps where both joints are highly flexed and when using linear relationships that consider only PIP joint angles. [ABSTRACT FROM AUTHOR]

Data gloves with strain gauges are a widely used technology to record hand kinematics. Several researchers have experienced problems when using data glove models to record distal interphalangeal (DIP) joints, mainly in relation to bad glove fitting. The aim of this work is to report the problems that arise when using one of these gloves (CyberGlove) and to determine an appropriate hand size to avoid these problems. First, static controlled postures of DIP joints and dynamic recordings while closing/opening the fist were taken using the data gloves on participants with different hand sizes, in order to establish the minimum hand length that does not pose recording problems. The minimum obtained hand length that allowed proper recording was 184 mm. Then, validation was performed, which consisted of recording the functional range of motion of the DIP joints in a sample of eight healthy participants with hand lengths longer than the minimum obtained one. These results were then compared to the results found in the literature. Although the glove fit properly, some problems remained: difficulty to record small flexion angles or a diminished touch sensitivity. Its usability would improve if two or three different glove sizes were commercially available. [ABSTRACT FROM AUTHOR]