Abstract
Simultaneous measurement of the kinematics of all hand segments is cumbersome due to sensor placement constraints, occlusions, and environmental disturbances. The aim of this study is to reduce the number of sensors required by using kinematic synergies, which are considered the basic building blocks underlying hand motions. Synergies were identified from the public KIN-MUS UJI database (22 subjects, 26 representative daily activities). Ten synergies per subject were extracted as the principal components explaining at least 95% of the total variance of the angles recorded across all tasks. The 220 resulting synergies were clustered, and candidate angles for estimating the remaining angles were obtained from these groups. Different combinations of candidates were tested and the one providing the lowest error was selected, its goodness being evaluated against kinematic data from another dataset (KINE-ADL BE-UJI). Consequently, the original 16 joint angles were reduced to eight: carpometacarpal flexion and abduction of thumb, metacarpophalangeal and interphalangeal flexion of thumb, proximal interphalangeal flexion of index and ring fingers, metacarpophalangeal flexion of ring finger, and palmar arch. Average estimation errors across joints were below 10% of the range of motion of each joint angle for all the activities. Across activities, errors ranged between 3.1% and 16.8%.
Highlights
The human hand is a complex biomechanical system, with an intricate kinematics provided by 19 joints, some with various degrees of freedom (DoF)
We identified the minimum set of hand DoF that best represent the hand kinematics, in order to reduce the number of DoF needed to record the whole hand kinematics without losing relevant information by using kinematic synergies
PIP index finger flexion was the DoF with the highest error (12.14 degrees, 9.8% with respect to its range of motion (RoM)) and relative abduction MCP4–5 was the DoF with the lowest error (3.89 degrees, 6% with respect to its RoM). These specifications could be helpful in designing simpler devices to record the whole hand kinematics, by reducing the number of DoF to be recorded, but obtaining the best possible estimation angles for the non-recorded DoF. In fields such as virtual reality, recording eight DoF would be sufficient to obtain the complete kinematics of the hand, considering that these estimations were made from common grasps and objects used in everyday life
Summary
The human hand is a complex biomechanical system, with an intricate kinematics provided by 19 joints, some with various degrees of freedom (DoF). This complexity is key for the versatility of the hand, enabling a large number of activities to be performed with a high level of precision. Electrogoniometers are commonly used in clinical practice to measure the range of motion (RoM) of joints [14]. Due to their size, they can only be used to record a few hand joints simultaneously and are invasive. Methods with one or two markers are prone to larger skin movements, because the joint heads of the fingers have many wrinkles in the skin [19,20]
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