Three-dimensional finger joint angles by hand posture and object properties

Background Claw toe is a common spastic deformity of the lower limbs, but no objective analysis has been reported to date. Objectives To establish a three-dimensional motion analysis method for toe movement in healthy individuals and in a patient with claw toe. Methods Twenty healthy individuals and one patient with claw toe were included in this cross-sectional study. Infrared reflective markers were attached to II toe of each participant. In the standing position and using a handrail, participants performed four elevations of the lower limb. The proximal interphalangeal (PIP) joint and metacarpophalangeal (MTP) joint angles were measured during the elevations using five cameras. The patient with hemiplegia underwent botulinum toxin therapy three times, with joint angles assessed before and at 1 month after each injection. Results No marker detachment occurred due to contact or other external factors, and all assessments were completed successfully. In healthy individuals, PIP and MTP joint angles showed no change during repeated lower limb elevations. In contrast, the patient with claw toe showed increased flexion at the PIP and MTP joints, with progressive PIP joint flexion with increasing number of repetitions, but the MTP joint angle was unchanged. Conclusion Three-dimensional motion analysis enabled evaluation of toe motion and distinguished joint behavior between healthy individuals and a patient with claw toe, before and after botulinum toxin therapy.

The metacarpophalangeal (MCP) joint of the dexterous robotic finger exhibits two-degree-of-freedom(2-DOF) rotation, thus conventional designs require two discrete joint angle sensors for joint position control. This multi-sensor configuration restricts compactness and integration in dexterous robotic finger design. By embedding a miniature permanent magnet in the joint mechanism, compact measurement of 2-DOF rotation angles is achievable. Using a single three-axis Hall-Effect sensor, a mapping between 3D magnetic flux distribution and the joint's 2-DOF angles is established, thereby enabling determination of the joint angle without external references.To realize high-sensitivity response, sensor's geometry parameters were optimized. Subsequently, an artificial neural network (ANN) was trained to perform inverse mapping from the measured 3D magnetic flux intensity back to the joint angles. Experimental validation confirms that this integrated sensor achieves a maximum absolute joint angle sensing error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.63^\circ$</tex-math></inline-formula>. This method offers a promising, compact solution for high-precision MCP joint angle sensing.

Hand pose estimation is recognised as being one of the most challenging topics in the field of human pose estimation. Accurate estimation and tracking of multi degree of freedom hand joints can be beneficial to many research areas such as robotic tele-manipulation, motion patterns, robotic hand design and, more generally, human computer/robot interaction. Current solutions to hand tracking are unsatisfactory due to numerous simplifications used in modeling of the hand kinematics and noise-prone hand and finger position sensing methods. In this paper, we propose alternative hand pose sensing approach that includes detecting palm shape in order to more accurately estimate joint angles of middle and index fingers and thumb. We use Inertia Measurement Unit (IMU) sensors on the palm to detect forming of palm arches in different fingers and thumbs’ poses. Principal component analysis as well as Dynamic Neural Networks are utilized to create three different models for fingers and thumb poses, while Polaris optical motion capture system is used as a ground truth. Validating through the unused data shows that using the palm shape as an additional parameter in hand tracking can estimate the hand digit joint angles with the average error of under 4.1%.

We employed an optimization method for the approximate entropy (ApEn) parameters ( , ) to evaluate the influence of changes in contraction intensity, visual feedback conditions, and joint angles in force ApEn during an isometric force-matching task. Seventeen participants performed an index finger abduction isometric force task in six contraction intensities (5-75% of maximum voluntary contraction, MVC), with and without visual feedback of the force, and in three different metacarpophalangeal (MCP) joint angles. Force variability, complexity (ApEn), and power spectrum density (PSD) were assessed, and a correlation analysis was performed between these variables. The best ApEn ( , ) pair for muscle force analysis was and force standard deviation (SD). Visual feedback influenced the ApEn; however, the comparison between experimental conditions (force intensity and joint angle) was similar. Both the force ApEn and the coefficient of variation (CoV) were reduced as a function of contraction intensity and without visual feedback. Conversely, the force SD and the PSD in the low-frequency band increased with contraction intensity and the absence of visual feedback. The changes in the MCP joint angle affected the MVC values and force CoV, with no significant effect on the force ApEn. The PSD in the low-frequency band (< 5Hz) showed a strong negative correlation with force ApEn in both visual feedback conditions. ApEn is influenced by force level and visual feedback, and it is strongly correlated with low-frequency force oscillations, which are related to the muscle's common drive.

The effect of three-dimensional postural change on shear elastic modulus of the iliotibial band

Innovative methods for finger gesture recognition have been an active research area, with surface electromyography (sEMG) emerging as a promising approach in human-machine interface applications, especially when visual imaging is impractical. However, sEMG-based gesture recognition is highly susceptible to movement artifacts, individual muscle activation, and changes in hand position, making dynamic gesture recognition challenging. While progress has been made in sEMG data collection and analysis, most studies focus on controlled, static hand positions, limiting real-world applicability. This study integrates a soft wearable sEMG sensor, a Video-Vision-Transform model, and motion sensor-based training to predict finger joint angles and recognize gestures across both static and dynamic hand positions. Despite inter-subject variability, results demonstrate differentiation of finger angles and gestures. For the highly performing subject, recognition accuracy reached 0.85 for static and 0.87 for dynamic settings. This work advances sEMG-based gesture recognition, indicating stable performance across tested static and dynamic conditions, suggesting potential suitability for natural and real-world applications.

The coordinates of the marked points of joints and the changes of foot force in the human walk motion are obtained with systems of motion image acquisition and force measuring. The effects of kinematics and dynamics on the human lower limb in different loads (0, 10, 20, 30 kg) and walking speeds (0.8, 1.3, 1.7 m/s) have been investigated using the human dynamics model,. The results were as follows. First, with the increasing load, the dorsiflexion angle of ankle joint increased during the procedure of toe-off, the range of motion of the knee joint decreased, the flexion angle of the hip joint increased, and oppositely, the extension angle of the hip joint decreased. Second, with the increasing speed, the range of motion of the ankle joint, the maximum flexion angle of the knee joint, the flexion angle, extension angle of the hip joint and the range of motion of hip joint increased. Finally, the torque and power of the ankle joint, the knee joint and the hip joint increased with the increasing speed and load. The results can provide an important basis for the mechanism design of lower limb power-assisted exoskeleton.

Body mass index affects knee joint mechanics during gait differently with and without moderate knee osteoarthritis

Objective. For the assessment of rheumatoid hand, three-dimensional (3D) malalignment including flexed or rotational deformities and dislocation at the finger joint is one of the important findings. The objective of this study is to prove usefulness of 3D computed tomography (3DCT) for the accurate measurement of palmoulnar flexion deformity at the metacarpophalangeal (MP) joint of the rheumatoid hand.Methods. Swanson implant arthroplasty at the 2nd through the 5th MP joints was performed at 179 joints in 46 hands of 40 patients with rheumatoid arthritis. Pre-and postoperatively, evaluations included Larsen grade and ulnar flexion angle by an x-ray; and ulnar flexion angle, palmar flexion angle, and resected bone length by a 3DCT.Results. With progression of Larsen grade and the joint dislocation, ulnar flexion angle increased. Average ulnar flexion angle was 18.7 ± 16.0° (mean ± SD) in grade III, 38.4 ± 21.2° in grade IV, and 40.1 ± 21.1° in grade V, 29.6 ± 16.0°in “subluxation,” 24.8 ± 21.2° in “dislocation,” and 41.1 ± 22.2° in “severe dislocation.” There was no significant difference between grade of the MP joint dislocation and palmar flexion angle. With progression of the MP joint dislocation, resected bone length increased.Conclusion. A 3DCT gives accurate information about deformity of the rheumatoid hand. Also, an appropriate length of bone resection can be determined in the preoperative planning.

SAT0082 The investigation for the influence of silastic arthroplasty of metacarpophalangeal joint on the active extension range of proximal interphalangeal joint in the rheumatoid hand

To evaluate the feasibility of delayed gadolinium-enhanced magnetic resonance (MR) imaging of the cartilage of metacarpophalangeal (MCP) joints in patients with rheumatoid arthritis (RA) compared with that in control subjects. Institutional review board approval and informed consent were obtained. Thirty-one MCP joints in 10 patients with RA (mean age, 59 years; range, 35-77 years) and six healthy volunteers (mean age, 51 years; range, 30-71 years) were examined with delayed gadolinium-enhanced MR imaging of cartilage. Sagittal images of the second and third MCP joints (hereafter, MCP II and MCP III) were acquired with a three-dimensional dual-flip-angle gradient-echo sequence at 3.0 T. B(1) field inhomogeneity-corrected T1 maps were calculated, and delayed gadolinium-enhanced MR imaging of cartilage values for phalangeal and metacarpal cartilage were determined. In addition, cartilage thickness was measured. A nonparametric Mann-Whitney U test was used to assess differences between groups. Phalangeal and metacarpal delayed gadolinium-enhanced MR imaging of cartilage values in patients with RA (MCP II: 388 msec ± 105 [standard deviation] and 342 msec ± 79, respectively; MCP III: 409 msec ± 96 and 371 msec ± 89, respectively) were significantly lower than in control subjects (MCP II: 598 msec ± 62 and 560 msec ± 51, respectively; MCP III: 586 msec ± 57 and 561 msec ± 80, respectively). Cartilage thickness of both joints was comparable in patients with RA (MCP II: 1.28 mm ± 0.50, MCP III: 1.17 mm ± 0.24) and control subjects (MCP II: 1.42 mm ± 0.33, MCP III: 1.18 mm ± 0.26). Delayed gadolinium-enhanced MR imaging of cartilage of the MCP joints is feasible at 3.0 T. Delayed gadolinium-enhanced MR imaging of cartilage may help to assess cartilage degeneration in morphologically normal-appearing MCP II and III cartilage in patients with RA.

The objective of this study was to evaluate the resting postures of the fingers and wrist based on the biomechanical model in term of hand posture (neutral, pronation, and supination) and gender (male and female). The finger and wrist joint angles were measured with VICON motion system. The EMG system was used to examine the muscle activity in the resting condition. The participants consisted of twenty male and twenty female students. The angles of the fingers and wrist were analyzed by means of the coordinate system associated with the International Society of Biomechanics. Hand posture was significant for all the joints. The finger and wrist joint flexed in supination more than in neutral and pronation. The hand posture and gender were not significant for the results of muscle activity, but it had larger muscle activities in supination more than in neutral and pronation.

The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is known to drift over time, resulting in drifting joint kinematics. This study proposes a novel joint kinematic estimation method that tightly incorporates the connection between adjacent segments within a sensor fusion algorithm, to obtain drift-free joint kinematics. Drift in the joint kinematics is eliminated solely by utilizing common information in the accelerometer and gyroscope measurements of sensors placed on connecting segments. Both an optimization-based smoothing and a filtering approach were implemented. Validity was assessed on a robotic manipulator under varying measurement durations and movement excitations. Standard deviations of the estimated relative sensor orientations were below 0.89° in an optimization-based smoothing implementation for all robot trials. The filtering implementation yielded similar results after convergence. The method is proven to be applicable in biomechanics, with a prolonged gait trial of 7 minutes on 11 healthy subjects. Three-dimensional knee joint angles were estimated, with mean RMS errors of 2.14°, 1.85°, 3.66° in an optimization-based smoothing implementation and mean RMS errors of 3.08°, 2.42°, 4.47° in a filtering implementation, with respect to a golden standard optical motion capture reference system.

In this paper, three experiments were conducted about the extension/flexion movements of the index finger at two speeds 0.4Hz and 0.8Hz of the 10 subjects. AR coefficients of sEMG signals were used as the feature parameters fed into the fuzzy neural network. The motion direction identification, fixed finger joint angle recognition and trajectory forecast are implemented in this study. The experimental results are generally satisfactory: the accuracy rate of motion direction identification is 100% (0.4Hz) and 92.5% (0.8Hz); the accuracy rates of fixed finger joint angle recognition all reached 100% in 0.4Hz and 0.8Hz experiments; joint angle forecast achieved a good trajectory tracking. The results of this study show the feasibility of extraction finger joint angle information from sEMG.

Caracterizou-se, mediante análise cinemática tridimensional baseada na videogrametria, o ângulo dorsal da articulação metacarpofalangiana de equinos em sete equinos da raça Puro Sangue Árabe. A análise tridimensional do movimento foi realizada em esteira rolante. O programa Dvideo foi utilizado para a obtenção das coordenadas tridimensionais do sistema de calibração e dos marcadores reflexivos posicionados na extremidade proximal do terceiro osso metacárpico, articulação metacarpofalangiana e extremidade distal da primeira falange. A articulação metacarpofalangiana apresentou extensão máxima durante o momento de apoio, no qual a face lateral do terceiro osso metacarpiano se apresentava de forma perpendicular ao solo. Foram observados dois picos de flexão durante a fase de elevação. Concluiu-se que a instrumentação utilizada para a análise cinemática tridimensional permitiu a investigação quantitativa da variação angular do movimento de extensão e flexão da articulação metacarpofalangiana de equinos por meio de imagens digitalizadas.