巴金森氏症是繼阿茲海默症之後，盛行率第二高的神經退化性疾病。其中手部症狀會影響患者的精細動作，並導致其心理壓力及社交障礙。而在MDS-UPDRS量表中以手部敲擊測試評估患者的運動功能和手部精細動作的速度與靈活性。而過往的文獻也提到手部敲擊測試有助於早期診斷巴金森氏症，因此也對手部的動作距離、動作速度等各項物理參數進行量化並分析。然而以無線感測技術對巴金森氏症手部運動症狀量化的研究仍寥寥無幾。
本研究透過通道狀態資訊商模型開啟以相位進行巴金森氏症患者手指拍打測試的手指距離量化方式，目標為穩定的偵測手指拍打動做的動作時長、動作次數及手指開合距離，以鐵板實驗模擬手指拍打運動情況並探討空間中通道狀態資訊的變化模式。在視距為2公尺，動作距離視距1公尺時，平均動作次數準確率可達99.44%，平均動作方向正確率達98.33%，平均手指輕敲動作時長準確率達89.27%，平均手指開合距離準確率達82.45%。
研究的目的是希望能在臨床或居家的環境中實現無接觸且高效的病情評估方法，為醫療健康監測領域提供新的技術支持，以此增進巴金森氏症病患的健康福祉。

Second only to Alzheimer's in prevalence, Parkinson's disease is a progressive neurodegenerative disorder that primarily affects fine motor skills in the hands, leading to psychological stress and social impairment. The MDS-UPDRS scale includes a hand tapping test to evaluate motor functions and the dexterity and speed of hand movements. Previous studies have indicated that this test is instrumental in the early diagnosis of Parkinson's by quantifying physical parameters such as movement distance and speed. However, research on quantifying Parkinson's hand motion symptoms using wireless sensing technology remains limited. This study introduces a novel method using Channel State Information (CSI) phase models to quantify the distance of finger tapping motions in Parkinson's patients. The aim is to stably detect the duration, frequency, and amplitude of finger tapping movements. Using a steel plate simulation to mimic finger tapping, the study observes changes in CSI within a space. The results demonstrate high accuracy rates at a 2-meter line-of-sight distance, with 99.44% accuracy for average motion frequency, 98.33% accuracy for motion direction, 89.27% accuracy for tapping duration, and 82.45% accuracy for finger opening and closing distances.
The objective of this research is to develop a non-contact, efficient assessment method that can be implemented in clinical or home settings, thereby providing new technical support in the medical monitoring field with the aim of enhancing the wellbeing of Parkinson's patients.

[1] 國家發展委員會. "國發會人口推估查詢系統-主要年齡別人口數." https://pop-proj.ndc.gov.tw/Custom_Detail_Search.aspx?n=39&t=1 (accessed.
[2] J. M. Fearnley and A. J. Lees, "Ageing and Parkinson's disease: substantia nigra regional selectivity," Brain, vol. 114 ( Pt 5), pp. 2283-301, Oct 1991
[3] C. G. Goetz et al., "Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results," Movement Disorders, vol. 23, no. 15, pp. 2129-70, Nov 15 2008
[4] L. M. D. Luiz, I. A. Marques, J. P. Folador, and A. O. Andrade, "Intra and inter-rater remote assessment of bradykinesia in Parkinson's disease," Neurología (English Edition), 10.1016/j.nrleng.2021.08.006 vol. 39, no. 4, pp. 345-352, 2024
[5] G. AlMahadin, A. Lotfi, E. Zysk, F. L. Siena, M. M. Carthy, and P. Breedon, "Parkinson's disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms - a patient and healthcare professional perspective," BMC Neurology, vol. 20, no. 1, p. 419, Nov 18 2020
[6] Z. Yang, Z. Zhou, and Y. Liu, "From RSSI to CSI: Indoor localization via channel response," ACM Computing Surveys, vol. 46, no. 2, p. Article 25, 2013
[7] C. L. Lin, W. J. Chang, and G. H. Tu, "Wi-Fi-Based Tracking of Human Walking for Home Health Monitoring," IEEE Internet Things J., vol. 9, no. 11, pp. 8935-8942, 2022
[8] Y. Xu, W. Yang, J. Wang, X. Zhou, H. Li, and L. Huang, "WiStep: Device-free Step Counting with WiFi Signals," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, no. 4, p. Article 172, 2018
[9] F. Zhang, C. Chen, B. Wang, and K. J. R. Liu, "WiSpeed: A Statistical Electromagnetic Approach for Device-Free Indoor Speed Estimation," IEEE Internet Things J., vol. 5, no. 3, pp. 2163-2177, 2018
[10] X. Liu, J. Cao, S. Tang, and J. Wen, "Wi-sleep: Contactless sleep monitoring via WiFi signals," in Proceedings - Real-Time Systems Symposium, 2015, vol. 2015-January, January ed., pp. 346-355
[11] X. Liu, J. Cao, S. Tang, J. Wen, and P. Guo, "Contactless Respiration Monitoring Via Off-the-Shelf WiFi Devices," IEEE Transactions on Mobile Computing, Article vol. 15, no. 10, pp. 2466-2479, 2016, Art no. 7345587
[12] A. Marcante et al., "Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson's Disease," Sensors (Basel), vol. 21, no. 1, Dec 28 2020
[13] "Dynamic Foot Pressure Measurement in Parkinson’s Disease with Foot Scan System," Journal of the Korean Medical Association, vol. 25, no. 2, pp. 172-179, 2007
[14] Y. R. Yang, Y. Y. Lee, S. J. Cheng, P. Y. Lin, and R. Y. Wang, "Relationships between gait and dynamic balance in early Parkinson's disease," Gait Posture, vol. 27, no. 4, pp. 611-5, May 2008
[15] T. Bikias, D. Iakovakis, S. Hadjidimitriou, V. Charisis, and L. J. Hadjileontiadis, "DeepFoG: An IMU-Based Detection of Freezing of Gait Episodes in Parkinson's Disease Patients via Deep Learning," Frontiers in Robotics and AI, vol. 8, p. 537384, 2021
[16] C. Caramia et al., "IMU-Based Classification of Parkinson's Disease From Gait: A Sensitivity Analysis on Sensor Location and Feature Selection," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 6, pp. 1765-1774, Nov 2018
[17] S. W. Chen et al., "Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis," BioMedical Engineering OnLine, vol. 10, p. 99, Nov 10 2011
[18] C.-W. Cho, W.-H. Chao, S.-H. Lin, and Y.-Y. Chen, "A vision-based analysis system for gait recognition in patients with Parkinson’s disease," Expert Systems with Applications, vol. 36, no. 3, Part 2, pp. 7033-7039, 2009/04/01/ 2009
[19] C. Barut, E. Kızıltan, E. Gelir, and F. Köktürk, "Advanced analysis of finger-tapping performance: a preliminary study," Balkan Journal of Medical Genetics, vol. 30, no. 2, pp. 167-71, Jun 2013
[20] J. Stamatakis et al., "Finger Tapping Clinimetric Score Prediction in Parkinson's Disease Using Low-Cost Accelerometers," Computational Intelligence and Neuroscience, vol. 2013, p. 717853, 2013/04/16 2013
[21] R. LeMoyne, T. Mastroianni, M. Cozza, C. Coroian, and W. Grundfest, "Implementation of an iPhone for characterizing Parkinson's disease tremor through a wireless accelerometer application," in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 31 Aug.-4 Sept. 2010 2010, pp. 4954-4958
[22] S. Summa et al., "Assessing bradykinesia in Parkinson's disease using gyroscope signals," in 2017 International Conference on Rehabilitation Robotics (ICORR), 17-20 July 2017 2017, pp. 1556-1561
[23] A. Channa, R.-C. Ifrim, D. Popescu, and N. Popescu, "A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson’s Patients," Sensors, vol. 21, no. 3, p. 981, 2021
[24] K. Niazmand, K. Tonn, A. Kalaras, U. M. Fietzek, J. H. Mehrkens, and T. C. Lueth, "Quantitative evaluation of Parkinson's disease using sensor based smart glove," in 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), 27-30 June 2011 2011, pp. 1-8
[25] T. Khan, D. Nyholm, J. Westin, and M. Dougherty, "A computer vision framework for finger-tapping evaluation in Parkinson's disease," Artificial Intelligence in Medicine, vol. 60, no. 1, pp. 27-40, 2014/01/01/ 2014
[26] Z. Guo et al., "Vision-Based Finger Tapping Test in Patients With Parkinson’s Disease via Spatial-Temporal 3D Hand Pose Estimation," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 8, pp. 3848-3859, 2022
[27] M. Youssef, M. Mah, and A. Agrawala, "Challenges: device-free passive localization for wireless environments," presented at the Proceedings of the 13th annual ACM international conference on Mobile computing and networking, Montréal, Québec, Canada, 2007
[28] Y. Zhang, Q. Liu, Y. Wang, and G. Yu, "CSI-Based Location-Independent Human Activity Recognition Using Feature Fusion," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-12, 2022
[29] Y. Zhang, Y. Yin, Y. Wang, J. Ai, and D. Wu, "CSI-based location-independent Human Activity Recognition with parallel convolutional networks," Computer Communications, vol. 197, pp. 87-95, 2023/01/01/ 2023
[30] H. Zou, Y. Zhou, J. Yang, W. Gu, L. Xie, and C. Spanos, "FreeDetector: Device-Free Occupancy Detection with Commodity WiFi," in 2017 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops), 12-12 June 2017 2017, pp. 1-5
[31] R. Zhou, M. Tang, Z. Gong, and M. Hao, "FreeTrack: Device-Free Human Tracking With Deep Neural Networks and Particle Filtering," IEEE Systems Journal, vol. 14, no. 2, pp. 2990-3000, 2020
[32] W. Wang, A. X. Liu, and M. Shahzad, "Gait recognition using wifi signals," presented at the Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 2016
[33] X. Zeng, B. Wang, C. Wu, S. D. Regani, and K. J. R. Liu, "Intelligent Wi-Fi Based Child Presence Detection System," in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 23-27 May 2022 2022, pp. 11-15
[34] K. Ali, A. X. Liu, W. Wang, and M. Shahzad, "Keystroke recognition using WiFi signals," in Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2015, vol. 2015-September, pp. 90-102
[35] D. Li, J. Liu, S. I. Lee, and J. Xiong, "LASense: Pushing the Limits of Fine-grained Activity Sensing Using Acoustic Signals," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 6, no. 1, p. Article 21, 2022
[36] X. Wang, C. Yang, and S. Mao, "PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices," in Proceedings - International Conference on Distributed Computing Systems, 2017, pp. 1230-1239
[37] Y. He, Y. Chen, Y. Hu, and B. Zeng, "WiFi Vision: Sensing, Recognition, and Detection With Commodity MIMO-OFDM WiFi," IEEE Internet Things J., Article vol. 7, no. 9, pp. 8296-8317, Sept 2020
[38] S. Tan and J. Yang, "WiFinger: Leveraging commodity WiFi for fine-grained finger gesture recognition," in Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2016, vol. 05-08-July-2016, pp. 201-210
[39] M. A. A. Al-qaness and F. Li, "WiGeR: WiFi-Based Gesture Recognition System," ISPRS International Journal of Geo-Information, vol. 5, no. 6, p. 92, 2016
[40] Y. B. Zhang, W. P. Sun, and M. Li, "WiRITE: General and Practical Wi-Fi Based Hand-Writing Recognition," IEEE Transactions on Mobile Computing, Article vol. 23, no. 4, pp. 2943-2957, Apr 2024
[41] Y. Zeng, D. Wu, J. Xiong, E. Yi, R. Gao, and D. Zhang, "FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, no. 3, p. Article 121, 2019
[42] S. D. Regani, B. B. Wang, Y. Q. Hu, and K. J. R. Liu, "GWrite: Enabling Through-the-Wall Gesture Writing Recognition Using WiFi," IEEE Internet Things J., Article vol. 10, no. 7, pp. 5977-5991, Apr 2023
[43] H. Wang et al., "Human respiration detection with commodity WiFi devices: Do user location and body orientation matter?," in UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016, pp. 25-36
[44] K. Qian, C. Wu, Y. Zhang, G. Zhang, Z. Yang, and Y. Liu, "Widar2.0: Passive Human Tracking with a Single Wi-Fi Link," presented at the Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, Munich, Germany, 2018
[45] D. Wu, D. Zhang, C. Xu, Y. Wang, and H. Wang, "WiDir: walking direction estimation using wireless signals," presented at the Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 2016
[46] L. Sun, S. Sen, D. Koutsonikolas, and K.-H. Kim, "WiDraw: Enabling Hands-free Drawing in the Air on Commodity WiFi Devices," presented at the Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, Paris, France, 2015
[47] D. Wu et al., "WiTraj: Robust Indoor Motion Tracking With WiFi Signals," IEEE Transactions on Mobile Computing, vol. 22, no. 5, pp. 3062-3078, 2023
[48] "Tracker Video Analysis and Modeling Tool for Physics Education," Physlets.org, 2024
[49] L. L. Rubchinsky, A. S. Kuznetsov, V. L. Wheelock, and K. A. Sigvardt, "Tremor," Scholarpedia, vol. 2, no. 10, p. 1379, 2007
[50] F. A. C. Brito et al., "The role of sex and handedness in the performance of the smartphone-based finger-tapping test," PLOS Digital Health, vol. 2, no. 8, p. e0000304, 2023
[51] D. Halperin, W. Hu, A. Sheth, and D. Wetherall, "Tool release: gathering 802.11n traces with channel state information," SIGCOMM Comput. Commun. Rev., vol. 41, no. 1, p. 53, 2011
[52] K. R. Liu and B. Wang, Wireless AI: Wireless Sensing, Positioning, IoT, and Communications. Cambridge University Press, 2019.