References

Adame, M Reyes, Ahmed Al-Jawad, Michailas Romanovas, Markus A Hobert, Walter Maetzler, Knut Möller, and Yiannos Manoli. 2012. “TUG Test Instrumentation for Parkinson’s Disease Patients Using Inertial Sensors and Dynamic Time Warping.” Biomedical Engineering/Biomedizinische Technik 57 (SI-1-Track-E): 1071–74. https://doi.org/10.1515/bmt-2012-4426.
Alsaify, Baha A, Mahmoud M Almazari, Rami Alazrai, and Mohammad I Daoud. 2020. “A Dataset for Wi-Fi-Based Human Activity Recognition in Line-of-Sight and Non-Line-of-Sight Indoor Environments.” Data in Brief 33: 106534. https://doi.org/10.1016/j.dib.2020.106534.
Ansai, Juliana Hotta, Ana Claudia Silva Farche, Paulo Giusti Rossi, Larissa Pires de Andrade, Theresa Helissa Nakagawa, and Anielle Cristhine de Medeiros Takahashi. 2019. “Performance of Different Timed up and Go Subtasks in Frailty Syndrome.” Journal of Geriatric Physical Therapy 42 (4): 287–93. https://doi.org/10.1519/JPT.0000000000000162.
Arrotta, Luca, Gabriele Civitarese, Riccardo Presotto, and Claudio Bettini. 2023. “DOMINO: A Dataset for Context-Aware Human Activity Recognition Using Mobile Devices.” In 2023 24th IEEE International Conference on Mobile Data Management (MDM), 346–51. IEEE. https://doi.org/10.1109/MDM58254.2023.00063.
Atlas, Les, Toshiteru Homma, and Robert Marks. 1987. An Artificial Neural Network for Spatio-Temporal Bipolar Patterns: Application to Phoneme Classification.” In Neural Information Processing Systems.
Beyea, James, Chris A McGibbon, Andrew Sexton, Jeremy Noble, and Colleen O’Connell. 2017. “Convergent Validity of a Wearable Sensor System for Measuring Sub-Task Performance During the Timed up-and-Go Test.” Sensors 17 (4): 934. https://doi.org/10.3390/S17040934.
Bland, J Martin, and DouglasG Altman. 1986. “Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement.” The Lancet 327 (8476): 307–10. https://doi.org/10.1016/S0140-6736(86)90837-8.
Boonstra, Tjeerd W, Jennifer Nicholas, Quincy JJ Wong, Frances Shaw, Samuel Townsend, and Helen Christensen. 2018. “Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions.” J. Med. Internet Res. 20 (7): e10131. https://doi.org/10.2196/10131.
Choi, Hyuckjin, Manato Fujimoto, Tomokazu Matsui, Shinya Misaki, and Keiichi Yasumoto. 2022. “Wi-CaL: WiFi Sensing and Machine Learning Based Device-Free Crowd Counting and Localization.” IEEE Access 10: 24395–410. https://doi.org/10.1109/ACCESS.2022.3155812.
Coelln, Rainer von et al. 2019. “Quantitative Mobility Metrics from a Wearable Sensor Predict Incident Parkinsonism in Older Adults.” Parkinsonism & Related Disorders 65: 190–96. https://doi.org/10.1016/J.PARKRELDIS.2019.06.012.
Coskun, Doruk, Ozlem Durmaz Incel, and Atay Ozgovde. 2015. “Phone Position/Placement Detection Using Accelerometer: Impact on Activity Recognition.” In 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 1–6. https://doi.org/10.1109/ISSNIP.2015.7106915.
Ester, Martin, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al. 1996. “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.” In Kdd, 96:226–31. 34.
Figo, Davide, Pedro C Diniz, Diogo R Ferreira, and Joao MP Cardoso. 2010. “Preprocessing Techniques for Context Recognition from Accelerometer Data.” Personal and Ubiquitous Computing 14: 645–62. https://doi.org/10.1007/s00779-010-0293-9.
Fukushima, Kunihiko. 1980. “Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position.” Biological Cybernetics 36 (4): 193–202. https://doi.org/10.1007/BF00344251.
Gholamiangonabadi, Davoud, Nikita Kiselov, and Katarina Grolinger. 2020. “Deep Neural Networks for Human Activity Recognition with Wearable Sensors: Leave-One-Subject-Out Cross-Validation for Model Selection.” IEEE Access 8: 133982–94. https://doi.org/10.1109/ACCESS.2020.3010715.
Goldsack, Jennifer C, Andrea Coravos, Jessie P Bakker, Brinnae Bent, Ariel V Dowling, Cheryl Fitzer-Attas, Alan Godfrey, et al. 2020. “Verification, Analytical Validation, and Clinical Validation (V3): The Foundation of Determining Fit-for-Purpose for Biometric Monitoring Technologies (BioMeTs).” Npj Digital Medicine 3 (1): 55. https://doi.org/10.1038/S41746-020-0260-4.
González-Pérez, Alberto, Miguel Matey-Sanz, Carlos Granell, and Sven Casteleyn. 2022. “Using Mobile Devices as Scientific Measurement Instruments: Reliable Android Task Scheduling.” Pervasive and Mobile Computing 81: 101550. https://doi.org/10.1016/j.pmcj.2022.101550.
González-Pérez, Alberto, Miguel Matey-Sanz, Carlos Granell, Laura Díaz-Sanahuja, Juana Bretón-López, and Sven Casteleyn. 2023. “AwarNS: A Framework for Developing Context-Aware Reactive Mobile Applications for Health and Mental Health.” Journal of Biomedical Informatics, 104359. https://doi.org/10.1016/j.jbi.2023.104359.
Gupta, Neha, Suneet K Gupta, Rajesh K Pathak, Vanita Jain, Parisa Rashidi, and Jasjit S Suri. 2022. “Human Activity Recognition in Artificial Intelligence Framework: A Narrative Review.” Artificial Intelligence Review 55 (6): 4755–4808. https://doi.org/10.1007/s10462-021-10116-x.
Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.
Hubel, D. H., and T. N. Wiesel. 1959. “Receptive Fields of Single Neurones in the Cat’s Striate Cortex.” The Journal of Physiology 148 (3): 574–91. https://doi.org/10.1113/jphysiol.1959.sp006308.
Jaén-Vargas, Milagros et al. 2022. “Effects of Sliding Window Variation in the Performance of Acceleration-Based Human Activity Recognition Using Deep Learning Models.” PeerJ Computer Science 8: e1052. https://doi.org/10.7717/peerj-cs.1052.
Koo, Terry K, and Mae Y Li. 2016. “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.” Journal of Chiropractic Medicine 15 (2): 155–63. https://doi.org/10.1016/J.JCM.2016.02.012.
Kruskal, William H., and W. Allen Wallis. 1952. “Use of Ranks in One-Criterion Variance Analysis.” Journal of the American Statistical Association 47 (260): 583–621. https://doi.org/10.1080/01621459.1952.10483441.
Ma, Yongsen, Gang Zhou, and Shuangquan Wang. 2019. “WiFi Sensing with Channel State Information: A Survey.” ACM Comput. Surv. 52 (3): 1–36. https://doi.org/10.1145/3310194.
Madhushri, Priyanka, Armen A Dzhagaryan, Emil Jovanov, and Aleksandar Milenkovic. 2016. “A Smartphone Application Suite for Assessing Mobility.” In 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3117–20. IEEE. https://doi.org/10.1109/EMBC.2016.7591389.
Mann, Henry B, and Donald R Whitney. 1947. “On a Test of Whether One of Two Random Variables Is Stochastically Larger Than the Other.” The Annals of Mathematical Statistics, 50–60.
Matey-Sanz, Miguel. 2022. Code and data resources for "Instrumented Timed Up and Go test using inertial sensors from consumer wearable devices".” Zenodo. https://doi.org/10.5281/zenodo.6405874.
———. 2023a. Reproducible package for "Temporal Stability on Human Activity Recognition based on Wi-Fi CSI".” Zenodo. https://doi.org/10.5281/zenodo.7991716.
———. 2023b. Reproducible Package for "Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition".” Zenodo. https://doi.org/10.5281/zenodo.8163542.
Matey-Sanz, Miguel, Sven Casteleyn, and Carlos Granell. 2023a. “Dataset of Inertial Measurements of Smartphones and Smartwatches for Human Activity Recognition.” Data in Brief 51: 109809. https://doi.org/10.1016/j.dib.2023.109809.
———. 2023b. Smartphone and smartwatch inertial measurements from heterogeneous subjects for human activity recognition. Zenodo. https://doi.org/10.5281/zenodo.8398688.
———. 2024a. “Background Sensors.” Zenodo. https://doi.org/10.5281/zenodo.10635734.
———. 2024b. “NativeScript WearOS Sensors.” Zenodo. https://doi.org/10.5281/zenodo.10640461.
———. 2024c. “WearOS Sensors.” Zenodo. https://doi.org/10.5281/zenodo.10640429.
Matey-Sanz, Miguel, and Alberto González-Pérez. 2022a. “TUG Test Smartphone Application.” Zenodo. https://doi.org/10.5281/zenodo.7456835.
———. 2022b. “TUG Test Smartwatch Application.” Zenodo. https://doi.org/10.5281/zenodo.7457098.
Matey-Sanz, Miguel, Alberto González-Pérez, Sven Casteleyn, and Carlos Granell. 2022. “Instrumented Timed up and Go Test Using Inertial Sensors from Consumer Wearable Devices.” In International Conference on Artificial Intelligence in Medicine, 144–54. Springer. https://doi.org/10.1007/978-3-031-09342-5\_14.
———. 2024. “Implementing and Evaluating the Timed up and Go Test Automation Using Smartphones and Smartwatches.” IEEE Journal of Biomedical and Health Informatics 28 (11): 6594–6605. https://doi.org/10.1109/JBHI.2024.3456169.
Matey-Sanz, Miguel, Joaquín Torres-Sospedra, Alberto González-Pérez, Sven Casteleyn, and Carlos Granell. 2024. “Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition.” In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 391–405. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-49018-7_28.
Matey-Sanz, Miguel, Joaquín Torres-Sospedra, and Adriano Moreira. 2023. “Temporal Stability on Human Activity Recognition Based on Wi-Fi CSI.” In 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), 1–6. https://doi.org/10.1109/IPIN57070.2023.10332214.
McCulloch, Warren S, and Walter Pitts. 1943. “A Logical Calculus of the Ideas Immanent in Nervous Activity.” The Bulletin of Mathematical Biophysics 5: 115–33. https://doi.org/10.1007/BF02478259.
McGraw, Kenneth O, and Seok P Wong. 1996. “Forming Inferences about Some Intraclass Correlation Coefficients.” Psychological Methods 1 (1): 30. https://doi.org/10.1037/1082-989X.1.1.30.
Milosevic, Mladen, Emil Jovanov, and Aleksandar Milenković. 2013. “Quantifying Timed-up-and-Go Test: A Smartphone Implementation.” In 2013 IEEE International Conference on Body Sensor Networks, 1–6. IEEE. https://doi.org/10.1109/BSN.2013.6575478.
Minh Dang, L., Kyungbok Min, Hanxiang Wang, Md. Jalil Piran, Cheol Hee Lee, and Hyeonjoon Moon. 2020. “Sensor-Based and Vision-Based Human Activity Recognition: A Comprehensive Survey.” Pattern Recognition 108: 107561. https://doi.org/https://doi.org/10.1016/j.patcog.2020.107561.
Podsiadlo, Diane, and Sandra Richardson. 1991. “The Timed ‘up & Go’: A Test of Basic Functional Mobility for Frail Elderly Persons.” Journal of the American Geriatrics Society 39 (2): 142–48. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x.
Rosenblatt, Frank. 1958. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.” Psychological Review 65 (6): 386. https://doi.org/10.1037/h0042519.
Sainath, Tara N., Oriol Vinyals, Andrew Senior, and Haşim Sak. 2015. “Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks.” In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4580–84. https://doi.org/10.1109/ICASSP.2015.7178838.
Salarian, Arash et al. 2010. “iTUG, a Sensitive and Reliable Measure of Mobility.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 18 (3): 303–10. https://doi.org/10.1109/TNSRE.2010.2047606.
Sansano, Emilio et al. 2020. “A Study of Deep Neural Networks for Human Activity Recognition.” Comput. Intell. 36 (3): 1113–39. https://doi.org/10.1111/coin.12318.
Shapiro, S. S., and M. B. Wilk. 1965. An analysis of variance test for normality (complete samples)†.” Biometrika 52 (3-4): 591–611. https://doi.org/10.1093/biomet/52.3-4.591.
Shi, Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun WOO. 2015. “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting.” In Advances in Neural Information Processing Systems, edited by C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett. Vol. 28. Curran Associates, Inc.
Stisen, Allan, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjærgaard, Anind Dey, Tobias Sonne, and Mads Møller Jensen. 2015. “Smart Devices Are Different: Assessing and Mitigating Mobile Sensing Heterogeneities for Activity Recognition.” In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, 127–40. https://doi.org/10.1145/2809695.2809718.
Student. 1908. “The Probable Error of a Mean.” Biometrika, 1–25. https://doi.org/10.2307/2331554.
Sztyler, Timo, and Heiner Stuckenschmidt. 2016. “On-Body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition.” In 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), 1–9. https://doi.org/10.1109/PERCOM.2016.7456521.
Vaizman, Yonatan, Katherine Ellis, and Gert Lanckriet. 2017. “Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches.” IEEE Pervasive Computing 16 (4): 62–74. https://doi.org/10.1109/MPRV.2017.3971131.
Weiss, Gary M, Kenichi Yoneda, and Thaier Hayajneh. 2019. “Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living.” IEEE Access 7: 133190–202. https://doi.org/10.1109/ACCESS.2019.2940729.
Welch, B. L. 1947. “The Generalization of ‘Student’s’ Problem When Several Different Population Variances Are Involved.” Biometrika 34 (1-2): 28–35. https://doi.org/10.1093/biomet/34.1-2.28.
Wilcoxon, Frank. 1945. “Individual Comparisons by Ranking Methods.” Biometrics Bulletin 1 (6): 80–83. https://doi.org/10.2307/3001968.
Yousefi, Siamak, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, and Shahrokh Valaee. 2017. “A Survey on Behavior Recognition Using WiFi Channel State Information.” IEEE Communications Magazine 55 (10): 98–104. https://doi.org/10.1109/MCOM.2017.1700082.
Zakaria, Nor Aini, Yutaka Kuwae, Toshiyo Tamura, Kotaro Minato, and Shigehiko Kanaya. 2015. “Quantitative Analysis of Fall Risk Using TUG Test.” Computer Methods in Biomechanics and Biomedical Engineering 18 (4): 426–37. https://doi.org/10.1080/10255842.2013.805211.