Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach
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Abstract
Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation.
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Cites
The Hospital Anxiety and Depression Scale
Zigmond, A. S., & Snaith, R. P. (1983) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.1983.tb09716.x · Cited by 47,139 · see citing works
Vos, T., Flaxman, A. D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., Shibuya, K., Salomon, J. A., Abdalla, S., Aboyans, V., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61729-2 · Cited by 8,291 · see citing works
A Dendrite Method for Cluster Analysis
Caliński, T., & Harabasz, J. (1974) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610927408827101 · Cited by 7,866 · see citing works
Burden of major musculoskeletal conditions.
(2003) · Europe PMC (PubMed Central) · Cited by 3,521 · see citing works
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 1× · see citing works
“Other analogous studies have indeed included a greater number of observations for each class [56,57], particularly when implementing a neural network approach [58].”
BackgroundHill, J. C., Whitehurst, D. G., Lewis, M., Bryan, S., Dunn, K. M., Foster, N. E., Konstantinou, K., Main, C. J., Mason, E., Somerville, S., et al. (2011) · The Lancet · doi:10.1016/s0140-6736(11)60937-9 · Cited by 1,306 · see citing works
Koes, B. W., van Tulder, M. W., Lin, C. C., Macedo, L. G., McAuley, J. H., & Maher, C. G. (2010) · European Spine Journal · doi:10.1007/s00586-010-1502-y · Cited by 1,301 · see citing works
Diagnosis and treatment of low back pain
Koes, B. W., van Tulder, M. W., & Thomas, S. (2006) · BMJ · doi:10.1136/bmj.332.7555.1430 · Cited by 1,287 · see citing works
A primary care back pain screening tool: identifying patient subgroups for initial treatment
Hill, J. C., Dunn, K. M., Lewis, M., Mullis, R., Main, C. J., Foster, N. E., & Hay, E. M. (2008) · Arthritis Care & Research · doi:10.1002/art.23563 · Cited by 1,052 · see citing works
Prognosis and prognostic research: application and impact of prognostic models in clinical practice
Moons, K. G. M., Altman, D. G., Vergouwe, Y., & Royston, P. (2009) · BMJ · doi:10.1136/bmj.b606 · Cited by 934 · see citing works
The Role of Dynamic Three-Dimensional Trunk Motion in Occupationally-Related Low Back Disorders
Marras, W. S., Lavender, S. A., Leurgans, S. E., Rajulu, S., Allread, W. G., Fathallah, F. A., & Ferguson, S. A. (1993) · Spine · doi:10.1097/00007632-199304000-00015 · Cited by 828 · see citing works
Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks
Jiang, W., & Yin, Z. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2806333 · Cited by 676 · see citing works
The visual analogue scale: Its use in pain measurement
Langley, G. B., & Sheppeard, H. (1985) · Rheumatology International · doi:10.1007/bf00541514 · Cited by 619 · see citing works
The burden of musculoskeletal conditions at the start of the new millennium.
(2003) · PubMed · Cited by 580 · see citing works
Halilaj, E., Rajagopal, A., Fiterau, M., Hicks, J. L., Hastie, T. J., & Delp, S. L. (2018) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2018.09.009 · Cited by 481 · see citing works
Laird, R. A., Gilbert, J., Kent, P., & Keating, J. L. (2014) · BMC Musculoskeletal Disorders · doi:10.1186/1471-2474-15-229 · Cited by 426 · cited 1× · see citing works
“On the other hand, recent advances in wearable sensor technology provide new opportunities to develop quantitative assessment methodologies, such as capturing and analyzing patients’ movements during walking or other daily life activities [22–26].”
Background(2018) · Europe PMC (PubMed Central) · Cited by 409 · see citing works
Quantitative analysis of gait in Parkinson patients: increased variability of stride length
Blin, O. J., Ferrandez, A., & Serratrice, G. (1990) · Journal of the Neurological Sciences · doi:10.1016/0022-510x(90)90184-o · Cited by 378 · see citing works
Givon, U., Zeilig, G., & Achiron, A. (2008) · Gait & Posture · doi:10.1016/j.gaitpost.2008.07.011 · Cited by 316 · see citing works
Varatharajan, R., Manogaran, G., Priyan, M. K., & Sundarasekar, R. (2017) · Cluster Computing · doi:10.1007/s10586-017-0977-2 · Cited by 277 · see citing works
Ambulatory Fall-Risk Assessment: Amount and Quality of Daily-Life Gait Predict Falls in Older Adults
van Schooten, K. S., Pijnappels, M. A. G. M., Rispens, S. M., Elders, P. J. M., Lips, P. T. A. M., & van Dieën, J. H. (2015) · The Journals of Gerontology Series A · doi:10.1093/gerona/glu225 · Cited by 260 · see citing works
(1974) · Communication in Statistics- Theory and Methods · doi:10.1080/03610928308827180 · Cited by 259 · see citing works
Bejek, Z., Paróczai, R., Illyés, Á., & Kiss, R. M. (2005) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1007/s00167-005-0005-6 · Cited by 257 · see citing works
Chronic low back pain measurement with visual analogue scales in different settings
Ogon, M., Krismer, M., Söllner, W., Kantner-Rumplmair, W., & Lampe, A. (1996) · Pain · doi:10.1016/0304-3959(95)00208-1 · Cited by 222 · see citing works
Dunn, K. M., & Croft, P. R. (2005) · Spine · doi:10.1097/01.brs.0000173900.46863.02 · Cited by 204 · see citing works
Beneciuk, J. M., Bishop, M. D., Fritz, J. M., Robinson, M. E., Asal, N. R., Nisenzon, A. N., & George, S. Z. (2013) · Physical Therapy · doi:10.2522/ptj.20120207 · Cited by 185 · see citing works
Dankaerts, W., O’Sullivan, P., Straker, L. M., Burnett, A., & Skouen, J. S. (2005) · Manual Therapy · doi:10.1016/j.math.2005.02.001 · Cited by 183 · see citing works
Two‐Group Classification Using Neural Networks*
Patuwo, E. B., Hu, M. Y., & Hung, M. S. (1993) · Decision Sciences · doi:10.1111/j.1540-5915.1993.tb00491.x · Cited by 182 · see citing works
The Classification of Anatomic- and Symptom-based Low Back Disorders Using Motion Measure Models
Marras, W. S., Parnianpour, M., Ferguson, S. A., Kim, J.-Y., Crowell, R. R., Bose, S., & Simon, S. R. (1995) · Spine · doi:10.1097/00007632-199512000-00013 · Cited by 163 · see citing works
Self-citationJellema, P., van der Windt, D., van der Horst, H. E., Twisk, J. W. R., Stalman, W. A. B., & Bouter, L. M. (2005) · BMJ · doi:10.1136/bmj.38495.686736.e0 · Cited by 148 · see citing works
Subgrouping patients with low back pain in primary care: Are we getting any better at it?
Foster, N. E., Hill, J. C., & Hay, E. M. (2010) · Manual Therapy · doi:10.1016/j.math.2010.05.013 · Cited by 141 · see citing works
Psychosocial Interventions for Low Back Pain in Primary Care
van der Windt, D., Hay, E. M., Jellema, P., & Main, C. J. (2008) · Spine · doi:10.1097/brs.0b013e31815e39f9 · Cited by 136 · see citing works
Al-Eisa, E. S., Egan, D., Deluzio, K. J., & Wassersug, R. J. (2006) · Spine · doi:10.1097/01.brs.0000201325.89493.5f · Cited by 136 · see citing works
An objective approach to quantifying pain behavior and gait patterns in low back pain patients
Keefe, F. J., & Hill, R. W. (1985) · Pain · doi:10.1016/0304-3959(85)90285-4 · Cited by 131 · see citing works
Main, C. J., Sowden, G., Hill, J. C., Watson, P., & Hay, E. M. (2011) · Physiotherapy · doi:10.1016/j.physio.2011.03.003 · Cited by 126 · see citing works
Hay, E. M., Dunn, K. M., Hill, J. C., Lewis, M., Mason, E. E., Konstantinou, K., Sowden, G., Somerville, S., Vohora, K., Whitehurst, D. G. T., et al. (2008) · BMC Musculoskeletal Disorders · doi:10.1186/1471-2474-9-58 · Cited by 123 · see citing works
Conforti, I., Mileti, I., Prete, Z. D., & Palermo, E. (2020) · Sensors · doi:10.3390/s20061557 · Cited by 103 · see citing works
Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test
Doheny, E. P., Walsh, C., Foran, T. G., Greene, B. R., Fan, C. W., Cunningham, C., & Kenny, R. A. (2013) · Gait & Posture · doi:10.1016/j.gaitpost.2013.05.013 · Cited by 100 · see citing works
Costa-Black, K. M., Loisel, P., Anema, J. R., & Pransky, G. (2010) · Best Practice & Research Clinical Rheumatology · doi:10.1016/j.berh.2009.11.007 · Cited by 86 · see citing works
Greene, B. R., Redmond, S. J., & Caulfield, B. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2539098 · Cited by 80 · see citing works
The Effect of Neck Pain on Cervical Kinematics, as Assessed in a Virtual Environment
Bahat, H. S., Weiss, P. L., & Laufer, Y. (2010) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2010.09.007 · Cited by 79 · see citing works
Taborri, J., Palermo, E., & Rossi, S. (2019) · Sensors · doi:10.3390/s19061461 · Cited by 79 · see citing works
Christe, G., Redhead, L., Legrand, T., Jolles, B. M., & Favre, J. (2016) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2016.05.015 · Cited by 78 · see citing works
Quantitative falls risk estimation through multi-sensor assessment of standing balance
Greene, B. R., McGrath, D., Walsh, L. C., Doheny, E. P., McKeown, D. J., Garattini, C., Cunningham, C., Crosby, L., Caulfield, B., & Kenny, R. A. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/12/2049 · Cited by 67 · see citing works
Feature selection for elderly faller classification based on wearable sensors
Howcroft, J., Kofman, J., & Lemaire, E. D. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0255-9 · Cited by 66 · see citing works
Papi, E., Bull, A. M. J., & McGregor, A. H. (2018) · Clinical Biomechanics · doi:10.1016/j.clinbiomech.2018.04.006 · Cited by 62 · see citing works
Waddell, G. F. (1982) · PubMed · Cited by 61 · see citing works
Impairment Magnification During Dynamic Trunk Motions
Marras, W. S., Lewis, K. E. K., Ferguson, S. A., & Parnianpour, M. (2000) · Spine · doi:10.1097/00007632-200003010-00009 · Cited by 47 · see citing works
Self-citationAbedi, M., Manshadi, F. D., Zavieh, M. K., Mousavi, S. J., Baghban, A. A., Montazeri, A., & Parnianpour, M. (2015) · Manual Therapy · doi:10.1016/j.math.2015.04.006 · Cited by 43 · see citing works
Self-citationAshouri, S., Abedi, M., Abdollahi, M., Manshadi, F. D., Parnianpour, M., & Khalaf, K. A. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.002 · Cited by 37 · see citing works
Self-citationPau, M., Coghe, G., Corona, F., Leban, B., Marrosu, M. G., & Cocco, E. E. (2015) · BioMed Research International · doi:10.1155/2015/916478 · Cited by 30 · see citing works
Quantification of a Meaningful Change in Low Back Functional Impairment
Ferguson, S. A., Marras, W. S., Burr, D. L., Woods, S. T., Mendel, E., & Gupta, P. (2009) · Spine · doi:10.1097/brs.0b013e3181b34764 · Cited by 18 · see citing works
Differences Among Outcome Measures in Occupational Low Back Pain
Ferguson, S. A., Marras, W. S., & Burr, D. L. (2005) · Journal of Occupational Rehabilitation · doi:10.1007/s10926-005-5940-4 · Cited by 17 · see citing works
A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone
Karvekar, S., Abdollahi, M., & Rashedi, E. (2019) · IEEE Western New York Image and Signal Processing Workshop (WNYISPW) · doi:10.1109/wnyipw.2019.8923100 · Cited by 15 · see citing works
Self-citationDavoudi, M., Shokouhyan, S. M., Abedi, M., Meftahi, N., Rahimi, A., Rashedi, E., Hoviattalab, M., Narimani, R., Parnianpour, M., & Khalaf, K. A. (2020) · Sensors · doi:10.3390/s20102902 · Cited by 13 · see citing works
Self-citationA reliability study of the new sensors for movement analysis (SHARIF–HMIS)
Abedi, M., Manshadi, F. D., Zavieh, M. K., Ashouri, S., Azimi, H., & Parnanpour, M. (2015) · Journal of Bodywork and Movement Therapies · doi:10.1016/j.jbmt.2015.10.004 · Cited by 6 · see citing works
Self-citationWaddell, G. F., & Bircher, M. (1985) · International Journal of Rehabilitation Research · doi:10.1097/00004356-198509001-00026 · Cited by 2 · see citing works
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Technologies in Home-Based Digital Rehabilitation: Scoping Review
Arntz, A. R., Weber, F., Handgraaf, M., Lällä, K., Korniloff, K., Murtonen, K.-P., Chichaeva, J., Kidritsch, A., Heller, M., Sakellari, Ε., et al. (2023) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/43615 · Cited by 138 · see citing works
Machine Learning in Pain Medicine: An Up-To-Date Systematic Review
Matsangidou, M., Liampas, A., Pittara, M., Pattichi, C. S., & Zis, P. (2021) · Pain and Therapy · doi:10.1007/s40122-021-00324-2 · Cited by 66 · see citing works
Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review
D’Antoni, F., Russo, F., Ambrosio, L., Bacco, L., Vollero, L., Vadalà, G., Merone, M., Papalia, R., & Denaro, V. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19105971 · Cited by 65 · see citing works
Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain
Hady, D. A. A., & El‐Hafeez, T. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-68798-6 · Cited by 49 · see citing works
Cascella, M., Leoni, M. L. G., Shariff, M. N., & Varrassi, G. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14090983 · Cited by 45 · see citing works
Jourdan, T., Debs, N., & Frindel, C. (2021) · Sensors · doi:10.3390/s21144808 · Cited by 43 · see citing works
IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review
Bo, F., Yerebakan, M., Dai, Y., Wang, W., Li, J., Hu, B., & Gao, S. (2022) · Healthcare · doi:10.3390/healthcare10071210 · Cited by 35 · see citing works
Smartphone-based human fatigue level detection using machine learning approaches
Karvekar, S., Abdollahi, M., & Rashedi, E. (2021) · Ergonomics · doi:10.1080/00140139.2020.1858185 · Cited by 37 · see citing works
Self-citationApplications of Artificial Intelligence in Pain Medicine
Abd-Elsayed, A. A., Robinson, C. L., Marshall, Z. J., Diwan, S. A., & Peters, T. (2024) · Current Pain and Headache Reports · doi:10.1007/s11916-024-01224-8 · Cited by 28 · see citing works
Okeibunor, J., Jaca, A., Iwu, C. J., Idemili-Aronu, N., Ba, H., Zantsi, Z. P., Ndlambe, A. M., Mavundza, E. J., Muneene, D., Wiysonge, C. S., et al. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1102185 · Cited by 32 · see citing works
Abdollahi, M., Rashedi, E., Jahangiri, S., Kuber, P. M., Azadeh-Fard, N., & Dombovy, M. (2024) · Italian National Conference on Sensors · doi:10.3390/s24030812 · Cited by 24 · see citing works
Self-citationSerena, R. S., Hinterwimmer, F., Burgkart, R., von Eisenhart‐Rothe, R., & Rueckert, D. (2024) · JMIR mhealth and uhealth · doi:10.2196/60521 · Cited by 26 · see citing works
Kinematic Analysis of 360° Turning in Stroke Survivors Using Wearable Motion Sensors
Abdollahi, M., Kuber, P. M., Shiraishi, M., Soangra, R., & Rashedi, E. (2022) · Sensors · doi:10.3390/s22010385 · Cited by 24 · see citing works
Self-citationBiomechanical Phenotyping of Chronic Low Back Pain: Protocol for BACPAC
Quirk, D. A., Johnson, M. E., Anderson, D., Smuck, M. W., Sun, R., Matthew, R. P., Bailey, J. F., Marras, W. S., Bell, K. M., Darwin, J., et al. (2022) · Pain Medicine · doi:10.1093/pm/pnac163 · Cited by 23 · see citing works
Artificial intelligence: a new cutting-edge tool in spine surgery
Kalanchiam, G. P., Chandramohan, T., Raman, M., & Kalyanasundaram, H. (2024) · Asian Spine Journal · doi:10.31616/asj.2023.0382 · Cited by 22 · see citing works
Villalba-Meneses, F., Guevara, C., Lojan, A. B., Gualsaquí, M. G., Arias-Serrano, I., Velásquez-López, P. A., Almeida-Galárraga, D. A., Tirado-Espín, A., Marín, J., & Marín-Zurdo, J. (2024) · Sensors · doi:10.3390/s24030831 · Cited by 22 · see citing works
Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis
Karvekar, S. (2024) · RIT Scholar Works (Rochester Institute of Technology) · doi:10.58044/kgcoe-ym24 · Cited by 21 · see citing works
Thiry, P., Houry, M., Philippe, L., Nocent, O., Buisseret, F., Dierick, F., Slama, R., Bertucci, W., Thévenon, A., & Simoneau-Buessinger, É. (2022) · Sensors · doi:10.3390/s22135027 · Cited by 20 · see citing works
Sankaran, R., Kumar, A. A., & Parasuram, H. (2022) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/09544119221122012 · Cited by 20 · see citing works
Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis
Rao, A. Z., Siddique, S. S., Mujib, M. D., Hasan, M. A., Alokaily, A. O., & Tahira, T. (2024) · IEEE Access · doi:10.1109/access.2024.3377111 · Cited by 13 · see citing works
Davoudi, M., Shokouhyan, S. M., Abedi, M., Meftahi, N., Rahimi, A., Rashedi, E., Hoviattalab, M., Narimani, R., Parnianpour, M., & Khalaf, K. A. (2020) · Sensors · doi:10.3390/s20102902 · Cited by 13 · see citing works
Self-citationTechnological advances in lower-limb tele-rehabilitation: A review of literature
Ettefagh, A., & Fekr, A. R. (2024) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/20556683241259256 · Cited by 11 · see citing works
Kim, Y. W., Cho, W. H., Joa, K., Jung, H. Y., & Lee, S. (2020) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519420400114 · Cited by 8 · see citing works
Oliosi, E., Júlio, A. C., Probst, P., Silva, L., Vilas‐Boas, J. P., Pinheiro, A. R., & Gamboa, H. F. S. (2024) · Sensors · doi:10.3390/s24144750 · Cited by 8 · see citing works
IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A ReviewPreprint
Bo, F., Yerebakan, M. O., Dai, Y., Wang, W., Li, J., Gao, S., & Boyi, H. (2022) · doi:10.36227/techrxiv.19200482 · Cited by 10 · see citing works
Integrating multidimensional data analytics for precision diagnosis of chronic low back pain
Vickery, S., Junker, F., Döding, R., Belavý, D. L., Angelova, M. N., Karmakar, C. K., Becker, L. A., Taheri, N., Pumberger, M., Reitmaier, S., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-93106-1 · Cited by 8 · see citing works
Farra, F. D., Cerfoglio, S., Porta, M., Pau, M., Galli, M., Lopomo, N. F., & Cimolin, V. (2025) · Applied Sciences · doi:10.3390/app15115931 · Cited by 8 · see citing works
Çakıt, E., & Karwowski, W. (2025) · IEEE Access · doi:10.1109/access.2025.3585773 · Cited by 8 · see citing works
Thiry, P., Nocent, O., Buisseret, F., Bertucci, W., Thévenon, A., & Simoneau-Buessinger, É. (2022) · Entropy · doi:10.3390/e24040437 · Cited by 7 · see citing works
Singh, M., & Sharma, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-09076-9_40 · Cited by 7 · see citing works
Classification of chronic pain using fMRI data: Unveiling brain activity patterns for diagnosis
Rejula, V., Anitha, J., & Robinson, B. (2023) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.4034 · Cited by 5 · see citing works
Wearable sensor technologies for individuals with back pain: a scoping review
Ryan, J., Bowden, E. E., Hancock, M. M., Power, K., Edwards, N., White, E. V., Fullwood, D. T., Mitchell, U., Bowden, J. A., & Bowden, A. E. (2025) · Pain Management · doi:10.1080/17581869.2025.2561394 · Cited by 5 · see citing works
Yang, X., Samsudin, S., Wang, Y., Yuan, Y., Kamalden, T. F. T., & Yaakob, S. S. N. (2023) · Sustainability · doi:10.3390/su15032542 · Cited by 5 · see citing works
Decoding Wilson disease: a machine learning approach to predict neurological symptoms
Yang, Y., Wang, G.-A., Fang, S., Li, X., Ding, Y., Song, Y., He, W., Rao, Z., Diao, K., Zhu, X., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1418474 · Cited by 5 · see citing works
Castellanos-Ruíz, J., Montealegre-Mesa, L. M., Martínez-Toro, B. D., Gallo-Serna, J. J., & Almanza-Fuentes, O. (2020) · Universidad y Salud · doi:10.22267/rus.212301.214 · Cited by 5 · see citing works
Rukavina, K., Staunton, J., Zinzalias, P., Skorić, M. K., Wu, K., Bannister, K., Rizos, A., & Chaudhuri, К. R. (2025) · PLoS ONE · doi:10.1371/journal.pone.0316563 · Cited by 4 · see citing works
Yu, X., Xu, X., Huang, Q., Zhu, G., Xu, F., Liu, Z., Su, L. C., Zheng, H., Chen, Z., Chen, Q., et al. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1176299 · Cited by 4 · see citing works
Kim, J. (2024) · Journal of Back and Musculoskeletal Rehabilitation · doi:10.3233/bmr-240059 · Cited by 4 · see citing works
Lumbopelvic Kinematics During Functional Tasks in a Chronic Low Back Pain Observational Cohort
Bell, K. M., Roos, R. E., Alfikri, Z. F., Anderst, W. J., Bailes, A. H., Clark, W. W., Cook, H. A., Darwin, J., Johnson, M. E., McKernan, G., et al. (2025) · JOR Spine · doi:10.1002/jsp2.70117 · Cited by 3 · see citing works
Gorce, P., & Jacquier-Bret, J. (2026) · Bioengineering · doi:10.3390/bioengineering13030298 · Cited by 3 · see citing works
IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A ReviewPreprint
Bo, F., Yerebakan, M. O., Dai, Y., Wang, W., Li, J., Gao, S., & Boyi, H. (2022) · doi:10.36227/techrxiv.19200482.v1 · Cited by 3 · see citing works
Jeong, S., Kim, S., & Park, K.-N. (2023) · Digital Health · doi:10.1177/20552076231186217 · Cited by 2 · see citing works
Personalized Patient Safety Management: Sensors and Real-Time Data Analysis
Uddin, M. J., & Munni, M. N. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-75490-7_10 · Cited by 3 · see citing works
Toward Maturity Assessment of SNAP Bean Crops: A Best-Case Greenhouse Scenario
Hassanzadeh, A., Murphy, S. P., Pethybridge, S. J., van Aardt, J., & Zhang, F. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323202 · Cited by 2 · see citing works
Research in Automatic Pain Assessment
Cascella, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-78833-8_3 · Cited by 1 · see citing works
Liikkanen, S., Jalkanen, T., Mäkinen, M., Huttunen, T., Kilpeläinen, R., Reijula, J., Heimonen, J., Mäkiniemi, A., Gentile, G., & Eccleston, C. (2025) · Frontiers in Virtual Reality · doi:10.3389/frvir.2025.1601823 · Cited by 1 · see citing works
Burjawi, T., El‐Ansary, D., Farragher, J. B., Tirosh, O., & Pranata, A. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106155 · Cited by 1 · see citing works
Carlosama, J., Zhinín-Vera, L., Guevara, C., Cadena-Morejón, C., Almeida-Galárraga, D. A., Ramírez-Cando, L., Landázuri, K. R., Tirado-Espín, A., Acosta-Vargas, P., & Villalba-Meneses, F. (2025) · Sensors · doi:10.3390/s25216665 · Cited by 1 · see citing works
Benítez‐Andrades, J. A., Laballos-González, L., Pujante-Otalora, L., Nieto-Marcos, S., Pinto‐Carral, A., & Álvarez, M. J. Á. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03384-6 · Cited by 1 · see citing works
Work Recognition and Movement Trajectory Acquisition Using a Multi-Sensing Wearable Device
Ogata, K., Tanaka, H., & Kourogi, M. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394239 · Cited by 1 · see citing works
A Primal-Dual-Based Power Control Approach for Capacitated Edge ServersPreprint
Qinghui, Z., Li, W., Su, Q., & Zhang, X. (2021) · arXiv (Cornell University) · doi:10.3390/s1010000 · Cited by 1 · see citing works
Assessing the Ergonomic Design of a New Back Table for Perioperative Nurses
Thakur, K., Kuber, P. M., Abdollahi, M., Wenker, D., Kandrikar, M., & Rashedi, E. (2021) · Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care · doi:10.1177/2327857921101077
Self-citationSmart Physiotherapy: Wie die Digitalisierung die Physiotherapie verändern wird
Arntz, A. R., Weber, F., & Grüneberg, C. (2024) · MSK – Muskuloskelettale Physiotherapie · doi:10.1055/a-2344-7769
Early Detection of Low Back Pain: A Machine Learning Approach with Enhanced Data Techniques
Haider, M., Hashmi, M. S. A., Ishaq, A., Narejo, K. R., & Jameel, A. (2024) · Indus journal of bioscience research. · doi:10.70749/ijbr.v2i02.396
Clinical Applications of AI in Pain Medicine
Cascella, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-78833-8_4
A Comprehensive Review of AI-Based Low Back Pain Assessment and Rehabilitation
Singh, M., Kumar, C., Ansari, M. S. A., & Govil, M. C. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-81339-9_15
Gong, L., Yu, M., & Ardakani, S. P. (2025) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eetpht.11.9109
Ria, M. A., Ratna, T. T., Purba, S. C., Kar, R., Karim, M., Ali, M. O., Chaity, E. J., Hossen, S., Imran, J. A., & Hasan, S. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001369
Chakraborty, J., & Ghosh, S. (2025) · Journal of Bodywork and Movement Therapies · doi:10.1016/j.jbmt.2025.05.061
Comparison of machine learning models for the identification of disc herniation
Garcia, J. H., Canchaya, W. T. B., & Ovalle, C. (2024) · doi:10.1145/3718391.3718440
Applying a Virtual World Café Method for Participatory Design of AI Systems
Corrigan-Kavanagh, E., Plumbley, M. D., & Frohlich, D. M. (2025) · Journal of Participatory Research Methods · doi:10.35844/001c.140957
Gong, L., Prasad, P. S., Leung, C. C., Kashyap, G. S., Ardakani, S. P., Clifford, R., Williams, P., & Yu, M. (2025) · IEEE Access · doi:10.1109/access.2025.3602153
Problems and prospects of using digital technologies in medical and social rehabilitation
Kozlova, U. S. (2025) · Bulletin of the Russian association of specialists in medical and social expert evaluation rehabilitation and rehabilitation industry · doi:10.63839/19992351-19/04
Hwang, U., Ahn, I.-K., Lee, Y.-J., Baek, H.-Y., Park, J.-H., Han, S. Y., Yoo, H.-I., & Kwon, O.-Y. (2025) · Digital Health · doi:10.1177/20552076251407128
Mhamed, I., Soussi, S., Sellami, W., & Beji, R. S. (2025) · IEEE International Conference on Advances in Data-Driven Analytics and Intelligent Systems (ADACIS) · doi:10.1109/adacis65663.2025.11437280
Lalwani, A., Gombatto, S. P., Velazquez, Y., Wyckoff, E., Yashwante, P., Patrick, K., Loh, K. J., Yu, R., & Farcas, E. (2026) · Sensors · doi:10.3390/s26123800
Predicting Low Back Pain Using Machine Learning Techniques: A Systematic Review
KÖSEOĞLU, D., & Akkoyunlu, M. C. (2026) · Acta Infologica · doi:10.26650/acin.1704458
Yashwante, P., Gombatto, S. P., Velazquez, Y., Wyckoff, E., Lalwani, A., Patrick, K., Loh, K. J., Farcas, E., & Yu, R. (2026) · Sensors · doi:10.3390/s26134187
Koltermann, J. J., Floessel, P., Wunderlich, F. C., Funke, J.-J., Kaplick, H., & Disch, A. C. (2026) · Sensors · doi:10.3390/s26165178
- Authors
- Abdollahi, M., Ashouri, S., Abedi, M., Azadeh-Fard, N., Parnianpour, M., Khalaf, K., & Rashedi, E.
- Affiliations
- Rochester Institute of Technology (US)
- RMIT University (AU)
- Shahid Beheshti University (IR)
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
- Sharif University of Technology (IR)
- Khalifa University of Science and Technology (AE)
- Corresponding author
- Rashedi, Ehsan
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-06-26
- Volume
- 20
- Issue
- 12
- Pages
- 3600
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7348921
- OpenAlex
- W3038007004
- Cited by
- 69
- References
- 57
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 18 · 2023: 7 · 2022: 10 · 2021: 6 · 2020: 4
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RMIT Research Repository (RMIT University Library) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Pharmacology › Musculoskeletal pain and rehabilitation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- non-specific low back pain, risk stratification, inertial measurement unit, trunk kinematics, balance, STarT Back Screening Tool, wearable sensors, trunk flexion and extension, personalized rehabilitation, prognostic tools
- MeSH terms
- Machine Learning, Adult, Biomechanical Phenomena, Humans, Middle Aged, Low Back Pain, Torso
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