Accumulating evidence indicates that the spatial error of human's hand localization appears subject-specific. However, whether the idiosyncratic pattern persists across time with good within-subject consistency has not been adequately examined. Here we measured the hand localization map by a Visual-matching task in multiple sessions over 2 days. Interestingly, we found that participants improved their hand localization accuracy when tested repetitively without performance feedback. Importantly, despite the reduction of average error, the spatial pattern of hand localization errors remained idiosyncratic. Based on individuals' hand localization performance, a standard convolutional neural network classifier could identify participants with good accuracy. Moreover, we did not find supporting evidence that participants' baseline hand localization performance could predict their motor performance in a visual Trajectory-matching task even though both tasks require accurate mapping of hand position to visual targets in the same workspace. Using a separate experiment, we not only replicated these findings but also ruled out the possibility that performance feedback during a few familiarization trials caused the observed improvement in hand localization. We conclude that the conventional hand localization test itself, even without feedback, can improve hand localization but leave the idiosyncrasy of hand localization map unchanged.

21Accumulating evidence indicates that the human's proprioception map appears 22 subject-specific. However, whether the idiosyncratic pattern persists across time with 23 good within-subject consistency has not been quantitatively examined. Here we 24 measured the proprioception by a hand visual-matching task in multiple sessions over 25 two days. We found that people improved their proprioception when tested 26 repetitively without performance feedback. Importantly, despite the reduction of 27 average error, the spatial pattern of proprioception errors remained idiosyncratic. 28Based on individuals' proprioceptive performance, a standard convolutional neural 29 network classifier could identify people with good accuracy. We also found that 30 subjects' baseline proprioceptive performance could not predict their motor 31 performance in a visual trajectory-matching task even though both tasks require 32 accurate mapping of hand position to visual targets in the same workspace. Using a 33 separate experiment, we not only replicated these findings but also ruled out the 34 possibility that performance feedback during a few familiarization trials caused the 35 observed improvement in proprioception. We conclude that the conventional 36 proprioception test itself, even without feedback, can improve proprioception but 37 leave the idiosyncrasy of proprioception unchanged. 38 39 40 Keywords 41 Proprioception, kinaesthesia, visuomotor mapping, motor performance, motor 42 learning 43 44 45 72 4 73 However, to our knowledge, the subject-specificity of the proprioception map has 74 never been systematically examined. Many previous studies reached their conclusions 75 by eyeballing of data (Brown et al. 2003b; van den Dobbelsteen et al. 2004; Smeets et 76 al. 2006). Other studies calculated the within-subject correlation coefficients between 77 measurements from different conditions and found they were significantly larger than 78 zero (Wann and Ibrahim 1992; Desmurget et al. 2000). However, this kind of 79 correlation results only shows the similarity between conditions as opposed to the 80 idiosyncrasy of proprioception maps between subjects. A couple of studies computed 81 the within-subject correlation of proprioception maps and the between-subject 82 correlation, but they did not compare these correlations, possibly due to a limited 83 number of participants (Helms Tillery et al. 1994; Vindras et al. 1998; Rincon-84 Gonzalez et al. 2011). In sum, no previous study has quantitively examined the 85 idiosyncrasy of the proprioception map, leaving the question open about to what 86 extent one's proprioception map can be distinguished from others'. 87 88 Proprioception underlies motor performance in various tasks (Rosenbaum 2009). 89 Recent studies also found that motor learning and proprioceptive training could 90 benefit each other if these two tasks were similar. Proprioceptive training by passively 91 moving one's hand around a target circle could improve the subsequent motor 92 learning of drawing the target (Wong et...

Hallux valgus (HV) is one of the most common forefoot deformities. Early diagnosis and proper evaluation of HV are important for timely management of HV. The purpose of our study was to estimate the radiographic parameters of HV using deep learning to assess the agreement of the predicted measurement with the actual radiographic measurement. There were 131 patients enrolled in this study. A total of 248 radiographs and 337 photographs of the feet were acquired. Radiographic parameters including the HV angle (HVA), M1-M2 angle, and M1-M5 angle were measured. We constructed a convolutional neural network using Xception and made it a regression model. Then, we fine-tuned the model using images of the feet and the radiographic parameters. The coefficient of determination (R2) and root mean squared error (RMSE) were calculated to evaluate the performance of the model. The radiographic parameters, including the HVA, M1-M2 angle, and M1-M5 angle were predicted with an R2=0.684, RMSE=7.91; R2=0.573, RMSE=3.29; R2=0.381, RMSE=5.80, respectively. The present study demonstrated that our model was able to predict the radiographic parameters of HV from photography. This study shows a potential application of deep learning for HV screening.