The number of people with hand disabilities caused by stroke is increasing every year. Developing a low-cost and easy-to-use data glove to capture the human hand motion can be used to assess the patient's hand ability in home environment.While a majority of existing hand motion capture methods are too complex to be used for patients in residential settings. This paper proposes a new sensor layout strategy using the inertial and magnetic measurement units (IMMUs) and designs a multi-sensor Kalman data fusion algorithm. The sensor layout strategy is optimized according to the inverse kinematics and the developed hand model, and the number of sensors can be significantly reduced from 12 in conventional systems to 6 in our system with the hand motion being completely and accurately reconstructed. Hand motion capture experiments were conducted on a healthy subject using the developed data glove. The hand motion can be restored completely and the hand gesture can be recognized with an accuracy of 85%. The results of a continuous hand movement indicate an average error under 15% compared with the common glove with full sensors. This new set with optimized sensor layout is promising for lower-cost and residential medical applications.
In
this work, two ionic liquids (ILs), 1-ethyl-3-methylimidazolium
bromide ([EMIM][Br]) and 1-ethyl-3-methylimidazolium diethylphosphate
([EMIM][DEP]), were used to separate the mixture of methanol and methyl
ethyl ketone. Isobaric vapor–liquid equilibrium data for the
binary system of methanol + methyl ethyl ketone and the ternary systems
of methanol + methyl ethyl ketone + [EMIM][Br] and methanol + methyl
ethyl ketone + [EMIM][DEP] were measured at 101.3 kPa. The results
showed that the two ILs could enhance the relative volatility of methyl
ethyl ketone to methanol. The more the content of ILs, the greater
the relative volatility of methyl ethyl ketone to methanol. The azeotropic
phenomenon of the methanol + methyl ethyl ketone system could be eliminated
as the content of ILs reached a specific value. The order of separation
ability of the two ILs is [EMIM][DEP] > [EMIM][Br]. Finally, the
activity
coefficient models including the NRTL model and the UNIFAC-Lei model
were used to correlate the experimental vapor–liquid equilibrium
data, and the correlated results showed that the NRTL model was more
suitable to correlate the experimental data.
The prediction of hand grasping and control of a robotic manipulator for hand activity training is of great significance to assist stroke patients to recover their biomechanical functions. However, the human hand and the figure joints have multiple degrees of freedom; therefore, it is complex to process and analyze all the collected data in hand modeling. To simplify the description of grasping activities, it is necessary to extract and decompose the principal components of hand actions. In this paper, the relationships among hand grasping actions are explored by extracting the postural synergy basis of hand motions, aiming to simplify hand grasping actions and reduce the data dimensions for robot control. A convolutional neural network (CNN)-based hand activity prediction method is proposed, which utilizes motion data to estimate hand grasping actions. The prediction results were then used to control a stimulated robotic model according to the extracted postural synergy basis. The prediction accuracy of the proposed method for the selected hand motions could reach up to 94% and the robotic model could be operated naturally based on patient’s movement intention, so as to complete grasping tasks and achieve active rehabilitation.
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