Page 98 - Read Online
P. 98
Shu et al. Intell Robot 2024;4(1):74-86 I http://dx.doi.org/10.20517/ir.2024.05 Page 86
deep architectures. IEEE J Biomed Health Inform 2022;26:1164–76. DOI
18. Hssayeni MD, Jimenez-Shahed J, Burack MA, Ghoraani B. Ensemble deep model for continuous estimation of Unified Parkinson’s
Disease Rating Scale III. Biomed Eng Online 2021;20:32. DOI
19. Petitjean F, Ketterlin A, Gançarski P. A global averaging method for dynamic time warping, with applications to clustering. Pattern
Recognit 2011;44:678–93. DOI
20. Iglesias G, Talavera E, BGonzález-Prieto Á, Mozo A, Gómez-Canaval S. Data augmentation techniques in time series domain: a survey
and taxonomy. Neural Comput Appl 2023;35:10123–45. DOI
21. Pal G, Goetz CG. Assessing bradykinesia in parkinsonian disorders. Front Neurol 2013;4:54. DOI
22. Kim JW, Kwon Y, Kim YM, et al. Analysis of lower limb bradykinesia in Parkinson’s disease patients. Geriatr Gerontol Int 2012;12:257–
64. DOI
23. Zhang X, Gao Y, Lin J, Lu CT. Tapnet: multivariate time series classification with attentional prototypical network. Proc AAAI Conf Artif
Intell 2020;34:6845–52. DOI
24. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273–97. DOI
25. Martinez-Manzanera O, Roosma E, Beudel M, Borgemeester RWK, van Laar T, Maurits NM. A method for automatic and objective
scoring of bradykinesia using orientation sensors and classification algorithms. IEEE Trans Biomed Eng 2016;63:1016–24. DOI
26. Shima K, Tsuji T, Kan E, Kandori A, Yokoe M, Sakoda S. Measurement and evaluation of finger tapping movements using magnetic
sensors. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2008 Aug 20-25;
Vancouver, Canada. IEEE; 2008. pp. 5628–31. DOI
27. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9:1735–80. DOI
28. Shawen N, O’Brien MK, Venkatesan S, et al. Role of data measurement characteristics in the accurate detection of Parkinson’s disease
symptoms using wearable sensors. J Neuroeng Rehabil 2020;17:52. DOI
29. Lee WL, Sinclair NC, Jones M, et al. Objective evaluation of bradykinesia in Parkinson’s disease using an inexpensive marker-less motion
tracking system. Physiol Meas 2019;40:014004. DOI
30. Wu Z, Gu H, Hong R, et al. Kinect-based objective evaluation of bradykinesia in patients with Parkinson’s disease. Digit Health
2023;9:20552076231176653. DOI
31. Datta S, Karmakar CK, Palaniswami M. Averaging methods using dynamic time warping for time series classification. In: 2020 IEEE
Symposium Series on Computational Intelligence (SSCI); 2020 Dec 01-04; Canberra, Australia. IEEE; 2020. pp. 2794-8. DOI
32. Wang T, Liu Z, Zhang T, Hussain SF, Waqas M, Li Y. Adaptive feature fusion for time series classification. Knowl Based Syst
2022;243:108459. DOI
33. Cheng X, Han P, Li G, Chen S, Zhang H. A novel channel and temporal-wise attention in convolutional networks for multivariate time
series classification. IEEE Access 2020;8:212247–57. DOI
34. Williams S, Relton SD, Fang H, et al. Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos. Artif
Intell Med 2020;110:101966. DOI
35. Li C, Yang H, Cheng L, et al. Quantitative assessment of hand motor function for post-stroke rehabilitation based on HAGCN and
multimodality fusion. IEEE Trans Neural Syst Rehab Eng 2022;30:2032–41. DOI