人體行走方向會影響 Wi-Fi 步態速度與距離的量化結果，但僅由速度或距離資訊，仍難以判斷受試者的移動方向與終點位置。相較於穿戴式裝置與影像分析，Wi-Fi 感測具有非接觸、低配戴負擔及隱私風險較低等優點。因此，本研究建立一套基於 Wi-Fi 通道狀態資訊(Channel State Information, CSI)的相對到達角(Angle of Arrival, AoA)追蹤方法，以提供步態量化模型所需的方向輔助資訊。
本研究使用 Intel Wi-Fi Link 5300 無線網卡，透過一發三收架構取得 CSI 資料，並依序進行相位校正、相位共軛乘法、子載波選取、卡爾曼濾波、靜態區間選取與靜態分量抑制。系統根據各時間窗的靜態子空間能量比例自動調整抑制係數 β，再利用多重訊號分類(Multiple Signal Classification, MUSIC)演算法建立人體行走期間的相對 AoA 時間序列。此外，本研究以影像標註建立真實值 AoA 曲線，評估 CSI-AoA 對人體行走角度變化的反映能力，並將相對 AoA 與既有步態速度及距離模型整合，以比較不同候選方向與候選終點。
實驗結果顯示，靜態轉動天線實驗的平均絕對角度誤差為 0.571°與 0.875°，證明本方法可反映已知角度變化。人體行走實驗中，基準中垂線與接收端偏移路徑的相對角度變化誤差百分比分別為 9.34%與 6.74%，顯示相對 AoA 可呈現人體行走期間的主要角度變化。進一步結合步態距離模型後，20 筆量測中有 17 筆選出與實際行走方向一致的候選結果，一致率為 85%。指定方向單程行走實驗亦顯示，當起終點具有足夠的相對角度變化時，平均終點位置誤差可介於 0.084 m 至 0.253 m。綜合而言，相對 AoA 可補充步態速度與距離模型所欠缺的方向資訊，並具有應用於非穿戴式步態方向與終點位移觀察的潛力。

Gait parameters, such as walking velocity and traveled distance, provide important information for describing human mobility. However, velocity and distance alone cannot determine movement direction or endpoint position. Conventional gait assessment methods commonly rely on wearable sensors or vision-based systems, which may be limited by wearing burden, deployment requirements, occlusion, and privacy concerns. Therefore, this study develops a contactless Wi-Fi Channel State Information (CSI)-based relative Angle of Arrival (AoA) tracking method to provide the directional information required by gait quantification models.
CSI data were collected using Intel Wi-Fi Link 5300 network interface cards with a one-transmitter and three-receiver antenna configuration. The proposed processing framework includes phase calibration, Phase Conjugate Multiplication, adaptive subcarrier selection, Kalman filtering, automatic static-interval selection, static component suppression, and sliding-window Multiple Signal Classification. The static suppression coefficient β was automatically estimated for each time window according to the residual energy in the static signal subspace, thereby reducing the influence of the Line-of-Sight path and fixed environmental reflections. A video-based Ground Truth AoA trajectory was established to evaluate whether the estimated relative AoA could reflect the angular changes caused by human walking. Relative AoA was further combined with a direction-dependent gait distance model to compare different candidate directions and endpoints.
In the rotating-antenna experiments, the mean absolute angular errors were 0.571° and 0.875° for counterclockwise and clockwise rotation, respectively. In the human walking experiments, the mean relative-angle-change errors were 9.34% for the perpendicular-bisector path and 6.74% for the receiver-offset path. Increasing the starting distance reduced the correspondence between the Ground Truth trajectory and the high-energy region of the MUSIC spectrum. After integrating relative AoA with gait distance estimation, 17 of 20 measurements selected the candidate direction consistent with the actual walking direction, corresponding to an agreement rate of 85%. In the one-way walking experiments, the mean endpoint position errors ranged from 0.084 to 0.500 m, with lower errors observed when the movement produced a sufficient relative angular change. These findings demonstrate that relative AoA can complement gait velocity and distance estimation by providing auxiliary information for interpreting walking direction and endpoint displacement.

[1] di Biase L, Di Santo A, Caminiti ML, De Liso A, Shah SA, Ricci L, Di Lazzaro V, "Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring," Sensors, vol. 20, no. 12, p. 3529, 2020.
[2] A. Salarian, H. Russmann, F. J. G. Vingerhoets, C. Dehollain, Y. Blanc, P. R. Burkhard, and K. Aminian, “Gait assessment in Parkinson’s disease: Toward an ambulatory system for long-term monitoring,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 8, pp. 1434–1443, 2004.
[3] Stenum J, Hsu MM, Pantelyat AY, Roemmich RT, "Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change," PLOS Digital Health, vol. 3, no. 3, p. e0000467, 2024.
[4] F. Zhang, C. Chen, B. Wang, and K. J. R. Liu, “WiSpeed: A statistical electromagnetic approach for device-free indoor speed estimation,” IEEE Internet of Things Journal, vol. 5, no. 3, pp. 2163–2177, 2018.
[5] C. Li, L. Liu, Z. Cao, and M. Zhang, “WiVelo: Fine-grained walking velocity estimation for Wi-Fi passive tracking,” in Proc. 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2022, pp. 172–180.
[6] D. Wu, D. Zhang, C. Xu, Y. Wang, and H. Wang, “WiDir: Walking direction estimation using wireless signals,” in Proc. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2016, pp. 351–362.
[7] D. Zhang, H. Wang, and D. Wu, “Toward centimeter-scale human activity sensing with Wi-Fi signals,” Computer, vol. 50, no. 1, pp. 48–57, 2017, doi: 10.1109/MC.2017.7.
[8] H. Wang, D. Zhang, J. Ma, Y. Wang, Y. Wang, D. Wu, T. Gu, and B. Xie, “Human respiration detection with commodity WiFi devices: Do user location and body orientation matter?,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’16), 2016, pp. 25–36, doi: 10.1145/2971648.2971744.
[9] J. Xiong and K. Jamieson, “ArrayTrack: A fine-grained indoor location system,” in Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’13), Lombard, IL, USA, 2013, pp. 71–84.
[10] J. Jankovic, “Parkinson’s disease: Clinical features and diagnosis,” Journal of Neurology, Neurosurgery & Psychiatry, vol. 79, no. 4, pp. 368–376, 2008.
[11] N. Al-Khammash, N. Al-Jabri, A. Albishi, A. Al-Onazi, S. Aseeri, F. Alotaibi, Y. Almazroua, and M. Albloushi, “Quality of life in patients with Parkinson’s disease: A cross-sectional study,” Cureus, vol. 15, no. 1, Art. no. e33989, 2023.
[12] M. E. Morris, R. Iansek, T. A. Matyas, and J. J. Summers, “Stride length regulation in Parkinson’s disease,” Brain, vol. 119, no. 2, pp. 551–568, 1996.
[13] M. Morris, R. Iansek, T. Matyas, and J. Summers, “Abnormalities in the stride length-cadence relation in parkinsonian gait,” Movement Disorders, vol. 13, no. 1, pp. 61–69, 1998.
[14] W. C. Yang, W. L. Hsu, R. M. Wu, T. W. Lu, and K. H. Lin, “Motion analysis of axial rotation and gait stability during turning in people with Parkinson’s disease,” Gait & Posture, vol. 44, pp. 83–88, 2016.
[15] S. Rahimpour, W. Gaztanaga, A. P. Yadav, S. J. Chang, M. O. Krucoff, I. Cajigas, D. A. Turner, and D. D. Wang, “Freezing of gait in Parkinson’s disease: Invasive and noninvasive neuromodulation,” Neuromodulation, vol. 24, no. 5, pp. 829–842, 2021.
[16] C. G. Goetz, B. C. Tilley, S. R. Shaftman, G. T. Stebbins, S. Fahn, P. Martinez-Martin, 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. 2129–2170, 2008.
[17] T. Müller and H. Russ, “Levodopa, motor fluctuations and dyskinesia in Parkinson’s disease,” Expert Opinion on Pharmacotherapy, vol. 7, no. 13, pp. 1715–1730, 2006.
[18] A. Khalili, A. H. Soliman, M. Asaduzzaman, and A. Griffiths, “Wi-Fi sensing: Applications and challenges,” The Journal of Engineering, vol. 2020, no. 3, pp. 87–97, 2020, doi: 10.1049/joe.2019.0790.
[19] Y. Ma, G. Zhou, and S. Wang, “WiFi sensing with channel state information: A survey,” ACM Computing Surveys, vol. 52, no. 3, Art. no. 46, pp. 1–36, 2019, doi: 10.1145/3310194.
[20] D. Halperin, W. Hu, A. Sheth, and D. Wetherall, “Tool release: Gathering 802.11n traces with channel state information,” ACM SIGCOMM Computer Communication Review, vol. 41, no. 1, p. 53, 2011, doi: 10.1145/1925861.1925870.
[21] C. Y. Lee, S. J. Kang, S. K. Hong, H. I. Ma, U. Lee, and Y. J. Kim, “A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson’s disease,” PLOS ONE, vol. 11, no. 7, Art. no. e0158852, 2016.
[22] T. Yu, Y. Kim, A. Lee, J. Park, H. Kim, and J. Kim, “Clinically informed automated assessment of finger tapping in Parkinson’s disease,” Sensors, vol. 23, no. 22, Art. no. 9149, 2023.
[23] L. Ma, S. Lin, J. Jin, Z. Wang, X. Wang, Z. Chen, Y. Ling, F. Zhang, K. Ren, and T. Feng, “Objective assessment of gait and posture symptoms in Parkinson’s disease using wearable sensors and machine learning,” Frontiers in Aging Neuroscience, vol. 17, Art. no. 1618764, 2025.
[24] W. Zhang, Y. Ling, Z. Chen, et al., “Wearable sensor-based quantitative gait analysis in Parkinson’s disease patients with different motor subtypes,” npj Digital Medicine, vol. 7, Art. no. 169, 2024.
[25] J. C. M. Schlachetzki, J. Barth, F. Marxreiter, J. Gossler, Z. Kohl, S. Reinfelder, H. Gassner, K. Aminian, B. M. Eskofier, J. Winkler, and J. Klucken, “Wearable sensors objectively measure gait parameters in Parkinson’s disease,” PLOS ONE, vol. 12, no. 10, Art. no. e0183989, 2017.
[26] A. J. Nelson, D. Zwick, S. Brody, C. Doran, L. Pulver, G. Rooz, M. Sadownick, R. Nelson, and J. Rothman, “The validity of the GaitRite and the Functional Ambulation Performance scoring system in the analysis of Parkinson gait,” NeuroRehabilitation, vol. 17, no. 3, pp. 255–262, 2002.
[27] C. C. Harro, M. J. Shoemaker, O. J. Frey, A. C. Gamble, K. B. Harring, K. L. Karl, et al., “Reliability and validity of force platform measures of balance impairment in individuals with Parkinson disease,” Physical Therapy, vol. 96, no. 12, pp. 1955–1964, 2016.
[28] 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. 5, pp. 7033–7039, 2009.
[29] A. P. Rocha, H. M. P. Choupina, M. C. Vilas-Boas, J. M. Fernandes, M. J. Rosas, R. Vaz, and J. P. S. Cunha, “Parkinson’s disease assessment based on gait analysis using an innovative RGB-D camera system,” in Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2014.
[30] M. Lu, K. Poston, A. Pfefferbaum, E. V. Sullivan, L. Fei-Fei, K. M. Pohl, J. C. Niebles, and E. Adeli, “Vision-based estimation of MDS-UPDRS gait scores for assessing Parkinson’s disease motor severity,” in Medical Image Computing and Computer Assisted Intervention—MICCAI 2020, vol. 12263, pp. 637–647, 2020.
[31] S.-F. Chuang, W.-R. Wu, and Y.-T. Liu, “High-resolution AoA estimation for hybrid antenna arrays,” IEEE Transactions on Antennas and Propagation, vol. 63, no. 7, pp. 2955–2968, 2015, doi: 10.1109/TAP.2015.2426795.
[32] M. Kotaru, K. Joshi, D. Bharadia, and S. Katti, “SpotFi: Decimeter level localization using WiFi,” in Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication (SIGCOMM ’15), 2015, pp. 269–282, doi: 10.1145/2829988.2787487.
[33] Y. Xie, J. Xiong, M. Li, and K. Jamieson, “mD-Track: Leveraging multi-dimensionality for passive indoor Wi-Fi tracking,” in Proceedings of the 25th Annual International Conference on Mobile Computing and Networking (MobiCom ’19), 2019, Art. no. 8, pp. 1–16, doi: 10.1145/3300061.3300133.
[34] R. O. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276–280, 1986, doi: 10.1109/TAP.1986.1143830.
[35] R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45, 1960, doi: 10.1115/1.3662552.