Walking detection for Parkinson’s disease patients and healthy control subjects measured with a smartphone accelerometer using mean amplitude deviation algorithm

Authors

• Milla Juutinen Faculty of Medicine and Health Technology, Tampere University
• Jari Ruokolainen Faculty of Business and Management, Tampere University; Unit of Neurology, Satasairaala Central Hospital, Wellbeing Services County of Satakunta; School of Technology and Innovations, University of Vaasa
• Juha Puustinen Unit of Neurology, Satasairaala Central Hospital, Wellbeing Services County of Satakunta
• Anu Holm Satakunta University of Applied Sciences
• Mark van Gils Faculty of Medicine and Health Technology, Tampere University
• Antti Vehkaoja Faculty of Medicine and Health Technology, Tampere University

DOI:

https://doi.org/10.23996/fjhw.156622

Keywords:

Parkinson's disease, gait, neurodegenerative diseases, smartphone, acceleration

Abstract

Parkinson's disease is a neurodegenerative disorder that affects mobility, leading to a decline in the patient's quality of life. Analyzing gait for these patients aims to improve the mobility of the Parkinson’s disease patients. The goal of this study was to validate mean amplitude deviation for detecting gait in Parkinson’s disease patients and healthy controls. This method is robust and orientation-independent and has accurate results on physical activity detection for different study populations. The novelty of this study is to use and evaluate a previously validated method with Parkinson’s disease patients instead of healthy young subjects as in earlier studies. We utilized inertial measurement unit data measured using smartphones from pre-existing datasets with pre-defined and labeled activities and free-living data containing continuously collected over three consecutive days. One dataset included 30 healthy adults, and the other two included in total of 62 and 68 Parkinson’s disease patients and 40 and 39 healthy controls, respectively. The sensitivity of the algorithm in a controlled measurement setting was 100% and 98.7% for healthy adults and a combined dataset of Parkinson’s disease patients and control subjects, respectively. Correspondingly, the specificity was 74.9% and 81.6%. Visual inspection of the free-living data showed that the algorithm provided durations and timings of walking activities, and walking took place during the daytime as anticipated for subjects with a typical daily rhythm. Median walking times were under ten minutes per hour. The results reached the same performance range as earlier studies with an orientation-independent approach, justifying the feasibility of this method. Therefore, this study validated the use of mean amplitude deviation for walking detection in Parkinson’s disease patients and healthy adults. Future research will utilize the detected walking segments in analyzing the motor symptoms of the disease aiming to improve patient well-being through identifying the needs for additional healthcare interventions.

References

Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K, Litvan I, Lang AE, Halliday G, Goetz CG, Gasser T, Dubois B, Chan P, Bloem BR, Adler CH, Deuschl G. MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord. 2015 Oct;30(12):1591-601. https://doi.org/10.1002/mds.26424

Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stern MB, Dodel R, Dubois B, Holloway R, Jankovic J, Kulisevsky J, Lang AE, Lees A, Leurgans S, LeWitt PA, Nyenhuis D, Olanow CW, Rascol O, Schrag A, Teresi JA, van Hilten JJ, LaPelle N; Movement Disorder Society UPDRS Revision Task Force. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord. 2008 Nov 15;23(15):2129-70. https://doi.org/10.1002/mds.22340

Cunningham L, Mason S, Nugent C, Moore G, Finlay D, Craig D. Home-based monitoring and assessment of Parkinson's disease. IEEE Trans Inf Technol Biomed. 2011 Jan;15(1):47-53. https://doi.org/10.1109/TITB.2010.2091142

Bao L, Intille SS. Activity recognition from user-annotated acceleration data. In: In: Ferscha A, Mattern F (eds). Pervasive Computing: Second International Conference, Pervasive 2004, Linz/Vienna, Austria, April 21-23, 2004. Lecture Notes in Computer Science, vol 3001. Berlin, Heidelberg: Springer; 2004. p. 1–17. https://doi.org/10.1007/978-3-540-24646-6_1

Kang W, Han Y. SmartPDR: Smartphone-based pedestrian dead reckoning for indoor localization. IEEE Sens J. 2015;15(5):2906–16. https://doi.org/10.1109/JSEN.2014.2382568

Zhao G, Wang X, Zhao H, Jiang Z. An improved pedestrian dead reckoning algorithm based on smartphone built-in MEMS sensors. AEU Inf J Electron C. 2023;168:154674. https://doi.org/10.1016/j.aeue.2023.154674

Hebenstreit F, Leibold A, Krinner S, Welsch G, Lochmann M, Eskofier BM. Effect of walking speed on gait sub phase durations. Hum Mov Sci. 2015 Oct;43:118-24. https://doi.org/10.1016/j.humov.2015.07.009

Quintero D, Lambert DJ, Villarreal DJ, Gregg RD. Real-Time Continuous Gait Phase and Speed Estimation from a Single Sensor. Control Technol Appl. 2017 Aug;2017:847-852. https://doi.org/10.1109/CCTA.2017.8062565

Soltani A, Aminian K, Mazza C, Cereatti A, Palmerini L, Bonci T, Paraschiv-Ionescu A. Algorithms for Walking Speed Estimation Using a Lower-Back-Worn Inertial Sensor: A Cross-Validation on Speed Ranges. IEEE Trans Neural Syst Rehabil Eng. 2021;29:1955-1964. https://doi.org/10.1109/TNSRE.2021.3111681

Mehrang S, Jauhiainen M, Pietil J, Puustinen J, Ruokolainen J, Nieminen H. Identification of Parkinson's Disease Utilizing a Single Self-recorded 20-step Walking Test Acquired by Smartphone's Inertial Measurement Unit. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2913-2916. https://doi.org/10.1109/EMBC.2018.8512921

Brajdic A, Harle R. Walk detection and step counting on unconstrained smartphones. In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing (UbiComp '13). New York, NY, USA: Association for Computing Machinery; 2013. p. 225–34. https://doi.org/10.1145/2493432.2493449

Iluz T, Gazit E, Herman T, Sprecher E, Brozgol M, Giladi N, Mirelman A, Hausdorff JM. Automated detection of missteps during community ambulation in patients with Parkinson's disease: a new approach for quantifying fall risk in the community setting. J Neuroeng Rehabil. 2014 Apr 3;11:48. https://doi.org/10.1186/1743-0003-11-48

Pham MH, Elshehabi M, Haertner L, Del Din S, Srulijes K, Heger T, Synofzik M, Hobert MA, Faber GS, Hansen C, Salkovic D, Ferreira JJ, Berg D, Sanchez-Ferro Á, van Dieën JH, Becker C, Rochester L, Schmidt G, Maetzler W. Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson's Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back. Front Neurol. 2017 Sep 4;8:457. https://doi.org/10.3389/fneur.2017.00457

Dijkstra B, Zijlstra W, Scherder E, Kamsma Y. Detection of walking periods and number of steps in older adults and patients with Parkinson's disease: accuracy of a pedometer and an accelerometry-based method. Age Ageing. 2008 Jul;37(4):436-41. https://doi.org/10.1093/ageing/afn097

Vähä-Ypyä H, Vasankari T, Husu P, Mänttäri A, Vuorimaa T, Suni J, Sievänen H. Validation of Cut-Points for Evaluating the Intensity of Physical Activity with Accelerometry-Based Mean Amplitude Deviation (MAD). PLoS One. 2015 Aug 20;10(8):e0134813. https://doi.org/10.1371/journal.pone.0134813

Vähä-Ypyä H, Vasankari T, Husu P, Suni J, Sievänen H. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clin Physiol Funct Imaging. 2015 Jan;35(1):64-70. https://doi.org/10.1111/cpf.12127

Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans Inf Technol Biomed. 2006 Jan;10(1):156-67. https://doi.org/10.1109/TITB.2005.856864

Lee HH, Choi S, Lee MJ. Step Detection Robust against the Dynamics of Smartphones. Sensors (Basel). 2015 Oct 26;15(10):27230-50. https://doi.org/10.3390/s151027230

Micó-Amigo ME, Bonci T, Paraschiv-Ionescu A, Ullrich M, Kirk C, Soltani A, Küderle A, Gazit E, Salis F, Alcock L, et al. Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium. J Neuroeng Rehabil. 2023 Jun 14;20(1):78. https://doi.org/10.1186/s12984-023-01198-5

Zhen T, Yan L, Yuan P. Walking gait phase detection based on acceleration signals using LSTM-DNN algorithm. Algorithms. 2019;12(12):253. https://doi.org/10.3390/a12120253

Aittasalo M, Vähä-Ypyä H, Vasankari T, Husu P, Jussila AM, Sievänen H. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents' physical activity irrespective of accelerometer brand. BMC Sports Sci Med Rehabil. 2015 Aug 7;7:18. https://doi.org/10.1186/s13102-015-0010-0

Gao Y, Haapala EA, Vanhala A, Sääkslahti A, Rantakokko M, Laukkanen A, Pesola AJ, Rantalainen T, Finni T. Sedentary Thresholds for Accelerometry-Based Mean Amplitude Deviation and Electromyography Amplitude in 7-11 Years Old Children. Front Physiol. 2019 Aug 7;10:997. https://doi.org/10.3389/fphys.2019.00997

Gao Y, Rantalainen T, Finni T, Portegijs E, Eronen J, Rantanen T, Rantakokko M. Daily Physical Activity and Sedentary Time Assessed by Acceleration Based on Mean Amplitude Deviation among Older People. Int J Environ Res Public Health. 2020 Sep 21;17(18):6887. https://doi.org/10.3390/ijerph17186887

Bakrania K, Yates T, Rowlands AV, Esliger DW, Bunnewell S, Sanders J, Davies M, Khunti K, Edwardson CL. Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches. PLoS One. 2016 Oct 5;11(10):e0164045. https://doi.org/10.1371/journal.pone.0164045

Haapala EA, Rantalainen T, Hesketh KD, Rodda CP, Duckham RL. Accelerometer-based osteogenic indices, moderate-to-vigorous and vigorous physical activity, and bone traits in adolescents. J Musculoskelet Neuronal Interact. 2022 Dec 1;22(4):514-523.

Yoneyama M, Mitoma H, Okuma Y. Accelerometry-based long-term monitoring of movement disorders: from diurnal gait behavior to nocturnal bed mobility. J Mech Med Biol. 2013;13(2):1350041. https://doi.org/10.1142/S0219519413500413

Juutinen M, Wang C, Zhu J, Haladjian J, Ruokolainen J, Puustinen J, Vehkaoja A. Parkinson's disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study. PLoS One. 2020 Jul 23;15(7):e0236258. https://doi.org/10.1371/journal.pone.0236258

Açıcı K, Erdaş ÇB, Aşuroğlu T, Toprak MK, Erdem H, Oğul H. A random forest method to detect Parkinson’s disease via gait analysis. In: Boracchi G, Iliadis L, Jayne C, Likas A (eds). Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer; 2017. p. 609–19. https://doi.org/10.1007/978-3-319-65172-9_51

Aşuroğlu T, Açıcı K, Erdaş ÇB, Toprak MK, Erdem H, Oğul H. Parkinson’s disease monitoring from gait analysis via foot-worn sensors. Biocybern Biomed Eng. 2018;38(3):760–72. https://doi.org/10.1016/j.bbe.2018.06.002

Faiem N, Asuroglu T, Acici K, Kallonen A, Van Gils M. Assessment of Parkinson’s Disease Severity Using Gait Data: A Deep Learning-Based Multimodal Approach. In: Särestöniemi M, et al. Digital Health and Wireless Solutions. Nordic Conference on Digital Health and Wireless Solutions 2024. Communications in Computer and Information Science, vol 2084. Springer; 2024. p. 29–48. https://doi.org/10.1007/978-3-031-59091-7_3

Reyes-Ortiz JL, Oneto L, Samà A, Parra X, Anguita D. Transition-aware human activity recognition using smartphones. Neurocomputing. 2016;171:754–67. https://doi.org/10.1016/j.neucom.2015.07.085

Jauhiainen M, Puustinen J, Mehrang S, Ruokolainen J, Holm A, Vehkaoja A, Nieminen H. Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study. JMIR Res Protoc. 2019 Mar 27;8(3):e12808. https://doi.org/10.2196/12808

UC Irvine. UCI Machine Learning Repository [Internet]. UC Irvine [cited 2022 Dec 7]. Available from: http://archive.ics.uci.edu/ml/index.php

McClain JJ, Abraham TL, Brusseau TA Jr, Tudor-Locke C. Epoch length and accelerometer outputs in children: comparison to direct observation. Med Sci Sports Exerc. 2008 Dec;40(12):2080-7. https://doi.org/10.1249/MSS.0b013e3181824d98

Wang H, Zhao J, Li J, Tian L, Tu P, Cao T, An Y, Wang K, Li S. Wearable Sensor‐Based Human Activity Recognition Using Hybrid Deep Learning Techniques. Secur Commun Netw. 2020;2020(1):2132138. https://doi.org/10.1155/2020/2132138

Marschollek M, Goevercin M, Wolf KH, Song B, Gietzelt M, Haux R, Steinhagen-Thiessen E. A performance comparison of accelerometry-based step detection algorithms on a large, non-laboratory sample of healthy and mobility-impaired persons. Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1319-22. https://doi.org/10.1109/IEMBS.2008.4649407