ABSTRACT
INTRODUCTION: Soft-robotic gloves have been developed to enhance grip to support stroke patients during daily life tasks. Studies showed that users perform tasks faster without the glove as compared to with the glove. It was investigated whether it is possible to detect grasp intention earlier than using force sensors to enhance the performance of the glove. METHODS: This was studied by distinguishing reach-to-grasp movements from reach movements without the intention to grasp, using minimal inertial sensing and machine learning. Both single-user and multi-user support vector machine classifiers were investigated. Data were gathered during an experiment with healthy subjects, in which they were asked to perform grasp and reach movements. RESULTS: Experimental results show a mean accuracy of 98.2% for single-user and of 91.4% for multi-user classification, both using only two sensors: one on the hand and one on the middle finger. Furthermore, it was found that using only 40% of the trial length, an accuracy of 85.3% was achieved, which would allow for an earlier prediction of grasp during the reach movement by 1200 ms. CONCLUSIONS: Based on these promising results, further research will be done to investigate the possibility to use classification of the movements in stroke patients.
ABSTRACT
BACKGROUND: Preparation of KBr tablets, used for Fourier transform infrared (FT-IR) analysis of urinary calculus composition, is time-consuming and often hampered by pellet breakage. We developed a new FT-IR method for urinary calculus analysis. This method makes use of a Golden Gate Single Reflection Diamond Attenuated Total Reflection sample holder, a computer library, and an artificial neural network (ANN) for spectral interpretation. METHODS: The library was prepared from 25 pure components and 236 binary and ternary mixtures of the 8 most commonly occurring components. The ANN was trained and validated with 248 similar mixtures and tested with 92 patient samples, respectively. RESULTS: The optimum ANN model yielded root mean square errors of 1.5% and 2.3% for the training and validation sets, respectively. Fourteen simple expert rules were added to correct systematic network inaccuracies. Results of 92 consecutive patient samples were compared with those of a FT-IR method with KBr tablets, based on an initial computerized library search followed by visual inspection. The bias was significantly different from zero for brushite (-0.8%) and the concomitantly occurring whewellite (-2.8%) and weddellite (3.8%), but not for ammonium hydrogen urate (-0.1%), carbonate apatite (0.5%), cystine (0.0%), struvite (0.4%), and uric acid (-0.1%). The 95% level of agreement of all results was 9%. CONCLUSIONS: The new Golden Gate method is superior because of its smaller sample size, user-friendliness, robustness, and speed. Expert knowledge for spectral interpretation is minimized by the combination of a library search and ANN prediction, but visual inspection remains necessary.