{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:54:09Z","timestamp":1773392049977,"version":"3.50.1"},"reference-count":107,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Neurodegenerative diseases (NDs) are a significant threat to human health. Numerous research demonstrated that patients with NDs might present with decreased balance, which is responsible for an increased risk of falling. As an emerging technology, wearable devices can detect falls and prevent privacy breaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To access the evolution of trends and technology in wearable devices to detect falls among patients with NDs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We screened PubMed and Web of Science (February 2023) to summarize the pathway of fall detection with any body-worn sensor. Included articles were required to be full-text and published in English. Documents were excluded if they; (1) only used wearable devices for fall cueing, (2) did not offer sufficient information for data extraction, (3) did not use patients with NDs, (4) only used non-wearable sensors or devices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The review identified 89 articles at the end of the procedure for data extraction. A wide variety existed in participant sample size (1\u2013131), sensor types, placement and algorithms. 97.75% of papers (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f87) used patients with Parkinson\u2019s disease as experimental subjects. 21.45% of studies attached devices on the ankle (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f19), with a clear preference for using multiple types of sensors (58.43% of studies,\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f52). As the most commonly used inertial measurement unit (IMU), 21 articles utilized accelerometers and gyroscopes to assess falls. 39.33% of studies (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f35) choose data set to verify the effectiveness of their algorithm. Machine learning algorithms have become prevalent since 2019, and the most commonly used algorithm was support vector machine (SVM) (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f17).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These results show that an increasing number of researchers examine the validation performance of their systems in non-real-time. The ankle was the preferred location among researchers, and there is a clear preference to use multiple types of sensors and machine learning algorithms to improve accuracy and immediacy. Future work should focus on other NDs instead of limiting to Parkinson\u2019s disease and consider an adequately studied population. A consensus on walking tasks and accuracy measurements is urgently needed. Performing studies in a simulated free-living environment for a specified time frame is advisable, with continuous real-time monitoring and assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic review registration<\/jats:title>\n                    <jats:p>PROSPERO, identifier (CRD42023405952).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnbot.2026.1766109","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:47:36Z","timestamp":1773384456000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The evolution of trends and technology in wearable sensors used to detect falls in people with neurodegenerative diseases: a systematic review"],"prefix":"10.3389","volume":"20","author":[{"given":"Yuanzheng","family":"Chen","sequence":"first","affiliation":[{"name":"Qingdao Preschool Education College","place":["Qingdao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tinghuai","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Sciences, The Hong Kong Polytechnic University","place":["Hong Kong SAR, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijie","family":"Lin","sequence":"additional","affiliation":[{"name":"ShanTou No.1 Middle School","place":["Shantou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Qingdao Preschool Education College","place":["Qingdao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/s41572-021-00280-3","article-title":"Parkinson disease-associated cognitive impairment","volume":"7","author":"Aarsland","year":"2021","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref2","first-page":"223","author":"Ahlrichs","year":"2016"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"2394","DOI":"10.1109\/TBME.2017.2655344","article-title":"Smart gait-aid glasses for Parkinson's disease patients","volume":"64","author":"Ahn","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"3287","DOI":"10.3390\/s18103287","article-title":"A validation study of freezing of gait (FoG) detection and machine-learning-based FoG prediction using estimated gait characteristics with a wearable accelerometer","volume":"18","author":"Aich","year":"2018","journal-title":"Sensors"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"e96675","DOI":"10.1371\/journal.pone.0096675","article-title":"Objective assessment of fall risk in Parkinson's disease using a body-fixed sensor worn for 3 days","volume":"9","author":"Aner","year":"2014","journal-title":"PLoS One"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"406","DOI":"10.3389\/fneur.2017.00406","article-title":"L-DOPA and freezing of gait in Parkinson's disease: objective assessment through a wearable wireless system","volume":"8","author":"Antonio","year":"2017","journal-title":"Front. Neurol."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1109\/TNSRE.2019.2933626","article-title":"Prediction of gait freezing in parkinsonian patients: a binary classification augmented with time series prediction","volume":"27","author":"Arami","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1007\/s40846-020-00512-z","article-title":"Validation of minimal number of force sensitive resistors to predict risk of falling during a timed up and go test","volume":"40","author":"Ayena","year":"2020","journal-title":"J. Med. Biol. Eng."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1109\/TNSRE.2015.2508960","article-title":"Home-based risk of falling assessment test using a closed-loop balance model","volume":"24","author":"Ayena","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/TITB.2009.2036165","article-title":"Wearable assistant for Parkinson's disease patients with the freezing of gait symptom","volume":"14","author":"Bachlin","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"537384","DOI":"10.3389\/frobt.2021.537384","article-title":"DeepFoG: an IMU-based detection of freezing of gait episodes in Parkinson's disease patients via deep learning","volume":"8","author":"Bikias","year":"2021","journal-title":"Front. Robot. AI"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"614","DOI":"10.3390\/s21020614","article-title":"Prediction of freezing of gait in Parkinson's disease using wearables and machine learning","volume":"21","author":"Borz\u00ec","year":"2021","journal-title":"Sensors"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s40860-019-00086-x","article-title":"Home monitoring of motor fluctuations in Parkinson's disease patients","volume":"5","author":"Borzi","year":"2019","journal-title":"J. Reliab. Intell. Environ."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.knosys.2017.10.017","article-title":"Deep learning for freezing of gait detection in Parkinson's disease patients in their homes using a waist-worn inertial measurement unit","volume":"139","author":"Camps","year":"2018","journal-title":"Knowledge-Based Syst."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.gaitpost.2016.08.018","article-title":"A smartphone-based architecture to detect and quantify freezing of gait in Parkinson's disease","volume":"50","author":"Capecci","year":"2016","journal-title":"Gait Posture"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"7316","DOI":"10.1016\/j.eswa.2013.07.031","article-title":"Design and evaluation of a device worn for fall detection and localization: application for the continuous monitoring of risks incurred by dependents in an Alzheimer's care unit","volume":"40","author":"Charlon","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"6446","DOI":"10.3390\/s21196446","article-title":"A comparative study of time frequency representation techniques for freeze of gait detection and prediction","volume":"21","author":"Cheng","year":"2021","journal-title":"Sensors"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1007\/s00702-019-02020-0","article-title":"A novel single-sensor-based method for the detection ofgait-cycle breakdown and freezing of gait in Parkinson's disease","volume":"126","author":"Chomiak","year":"2019","journal-title":"J. Neural Transm."},{"key":"ref19","author":"Cole","year":"2011"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"6819","DOI":"10.3390\/s140406819","article-title":"Detection of freezing of gait in Parkinson disease: preliminary results","volume":"14","author":"Coste","year":"2014","journal-title":"Sensors"},{"key":"ref21","first-page":"827","article-title":"A waist-worn inertial measurement unit for long-term monitoring of Parkinson's disease patients","author":"Daniel","year":"2017"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"2444","DOI":"10.1109\/jbhi.2019.2952618","article-title":"Toward a wearable system for predicting freezing of gait in people affected by Parkinson's disease","volume":"24","author":"Demrozi","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"pzac129","DOI":"10.1093\/ptj\/pzac129","article-title":"Daily-living freezing of gait as quantified using wearables in people with Parkinson disease: comparison with self-report and provocation tests","volume":"102","author":"Denk","year":"2022","journal-title":"Phys. Ther."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1109\/TNSRE.2013.2287241","article-title":"Automatic identification and classification of freezing of gait episodes in Parkinson's disease patients","volume":"22","author":"Djuric-Jovicic","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.parkreldis.2020.06.018","article-title":"Life expectancy of parkinsonism patients in the general population","volume":"77","author":"Dommershuijsen","year":"2020","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"720516","DOI":"10.3389\/fneur.2021.720516","article-title":"Real-time detection of freezing motions in Parkinson's patients for adaptive gait phase synchronous cueing","volume":"12","author":"Dvorani","year":"2021","journal-title":"Front. Neurol."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1080\/0952813X.2018.1519000","article-title":"Discrete wavelet transform-based freezing of gait detection in Parkinson's disease","volume":"33","author":"El-Attar","year":"2021","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"138120","DOI":"10.1109\/access.2021.3117543","article-title":"Impact of data preparation in freezing of gait detection using feature-less recurrent neural network","volume":"9","author":"Esfahani","year":"2021","journal-title":"IEEE Access"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"100068","DOI":"10.1016\/j.iot.2019.100068","article-title":"IoT-based freezing of gait detection using grey relational analysis","volume":"13","author":"Ghosh","year":"2021","journal-title":"Internet Things"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"6540","DOI":"10.3390\/s25216540","article-title":"Fall detection in elderly people: a systematic review of ambient assisted living and smart home-related technology performance","volume":"25","author":"Gorce","year":"2025","journal-title":"Sensors (Basel)"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"2055668317750811","DOI":"10.1177\/2055668317750811","article-title":"Longitudinal assessment of falls in patients with Parkinson's disease using inertial sensors and the timed up and go test","volume":"5","author":"Greene","year":"2018","journal-title":"J. Rehabilitat. Assistive Technol. Eng."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"1256682","DOI":"10.3389\/fnins.2023.1256682","article-title":"Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions","volume":"17","author":"Guerra","year":"2023","journal-title":"Front. Neurosci."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"105629","DOI":"10.1016\/j.compbiomed.2022.105629","article-title":"High-accuracy wearable detection of freezing of gait in Parkinson's disease based on pseudo-multimodal features","volume":"146","author":"Guo","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"2077","DOI":"10.1109\/tnsre.2019.2938301","article-title":"The detection of freezing of gait in Parkinson's disease using asymmetric basis function TV-ARMA time-frequency spectral estimation method","volume":"27","author":"Guo","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2021.3090153","article-title":"Predicting state transition in freezing of gait via acceleration measurements for controlled cueing in Parkinson's disease","volume":"70","author":"Halder","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref36","first-page":"124","article-title":"Prediction of freezing of gait in patients with Parkinson's disease using EEG signals","volume":"246","author":"Handojoseno","year":"2018","journal-title":"Stud. Health Technol. Inform."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1038\/539179a","article-title":"Neurodegenerative diseases","volume":"539","author":"Heemels","year":"2016","journal-title":"Nature"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s12938-016-0194-x","article-title":"Pre-impact fall detection","volume":"15","author":"Hu","year":"2016","journal-title":"Biomed. Eng. Online"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"7439","DOI":"10.3390\/s24237439","article-title":"A review on assisted living using wearable devices","volume":"24","author":"Iadarola","year":"2024","journal-title":"Sensors (Basel)"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1049\/htl.2016.0064","article-title":"Fuzzy logic-based risk of fall estimation using smartwatch data as a means to form an assistive feedback mechanism in everyday living activities","volume":"3","author":"Iakovakis","year":"2016","journal-title":"Healthcare Technol. Lett."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/1743-0003-11-48","article-title":"Automated detection of missteps during community ambulation in patients with Parkinson's disease: a new approach for quantifying fall risk in the community setting","volume":"11","author":"Iluz","year":"2014","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref42","author":"Jovanov","year":"2009"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1089\/tmj.2017.0215","article-title":"Validation of freezing-of-gait monitoring using smartphone","volume":"24","author":"Kim","year":"2018","journal-title":"Telemed. E-Health"},{"key":"ref44","author":"Kim","year":"2015"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1038\/d41573-019-00006-x","article-title":"An exercise-linked mediator of memory protection","volume":"18","author":"Kingwell","year":"2019","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"1899","DOI":"10.1109\/jsen.2017.2659780","article-title":"Reliable and robust detection of freezing of gait episodes with wearable electronic devices","volume":"17","author":"Kita","year":"2017","journal-title":"IEEE Sensors J."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.patrec.2020.09.011","article-title":"A new machine learning based approach to predict freezing of gait","volume":"140","author":"Kleanthous","year":"2020","journal-title":"Pattern Recogn. Lett."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.2147\/CIA.S69773","article-title":"A practical method for the detection of freezing of gait in patients with Parkinson's disease","volume":"9","author":"Kwon","year":"2014","journal-title":"Clin. Interv. Aging"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.3390\/electronics9111919","article-title":"Improved deep learning technique to detect freezing of gait in Parkinson's disease based on wearable sensors","volume":"9","author":"Li","year":"2020","journal-title":"Electronics"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"2205","DOI":"10.3390\/s25072205","article-title":"A decade of progress in wearable sensors for fall detection (2015-2024): a network-based visualization review","volume":"25","author":"Li","year":"2025","journal-title":"Sensors"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1371\/journal.pmed.1000100","article-title":"The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration","volume":"6","author":"Liberati","year":"2009","journal-title":"BMJ"},{"key":"ref52","author":"Ly","year":"2017"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12984-020-00774-3","article-title":"Measuring freezing of gait during daily-life: an open-source, wearable sensors approach","volume":"18","author":"Mancini","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref54","doi-asserted-by":"publisher","first-page":"128","DOI":"10.3390\/s21010128","article-title":"Foot pressure wearable sensors for freezing of gait detection in Parkinson's disease","volume":"21","author":"Marcante","year":"2021","journal-title":"Sensors"},{"key":"ref55","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neucom.2014.09.084","article-title":"Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer","volume":"164","author":"Mart\u00edn","year":"2015","journal-title":"Neurocomputing"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1909.03428","article-title":"Feature-set-engineering for detecting freezing of gait in Parkinson's disease using deep recurrent neural networks","author":"Masiala","year":"2019","journal-title":"arXiv"},{"key":"ref57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pmcj.2015.12.007","article-title":"The role of wrist-mounted inertial sensors in detecting gait freeze episodes in Parkinson's disease","volume":"33","author":"Mazilu","year":"2016","journal-title":"Pervasive Mob. Comput."},{"key":"ref58","doi-asserted-by":"publisher","first-page":"5:1","DOI":"10.1145\/2701431","article-title":"A wearable assistant for gait training for Parkinsons disease with freezing of gait in out-of-the-lab environments","volume":"5","author":"Mazilu","year":"","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"ref59","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.1109\/JBHI.2015.2465134","article-title":"Prediction of freezing of gait in Parkinson's from physiological wearables: an exploratory study","volume":"19","author":"Mazilu","year":"","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"948","DOI":"10.3390\/s19040948","article-title":"Wearable sensors system for an improved analysis of freezing of gait in Parkinson's disease using electromyography and inertial signals","volume":"19","author":"Mazzetta","year":"2019","journal-title":"Sensors"},{"key":"ref61","doi-asserted-by":"publisher","first-page":"2613","DOI":"10.3390\/s22072613","article-title":"A multi-modal analysis of the freezing of gait phenomenon in Parkinson's disease","volume":"22","author":"Mesin","year":"2022","journal-title":"Sensors"},{"key":"ref62","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1109\/TBCAS.2019.2914253","article-title":"A wearable, patient-adaptive freezing of gait detection system for biofeedback cueing in Parkinson's disease","volume":"13","author":"Miko","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref63","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.jneumeth.2007.08.023","article-title":"Ambulatory monitoring of freezing of gait in Parkinson's disease","volume":"167","author":"Moore","year":"2007","journal-title":"Mov. Disord."},{"key":"ref64","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/1743-0003-10-19","article-title":"Autonomous identification of freezing of gait in Parkinson's disease from lower-body segmental accelerometry","volume":"10","author":"Moore","year":"2013","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref65","doi-asserted-by":"publisher","first-page":"110905","DOI":"10.1016\/j.exger.2020.110905","article-title":"Parkinsonian gait in aging: a feature of Alzheimer's pathology?","volume":"134","author":"Morel","year":"2020","journal-title":"Exp. Gerontol."},{"key":"ref66","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neucom.2011.09.037","article-title":"A survey on fall detection: principles and approaches","volume":"100","author":"Mubashir","year":"2013","journal-title":"Neurocomputing"},{"key":"ref67","doi-asserted-by":"publisher","first-page":"3898","DOI":"10.3390\/s19183898","article-title":"Towards real-time prediction of freezing of gait in patients with Parkinson's disease: addressing the class imbalance problem","volume":"19","author":"Naghavi","year":"2019","journal-title":"Sensors"},{"key":"ref68","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1109\/TNSRE.2019.2910165","article-title":"Prediction of freezing of gait in Parkinson's disease using statistical inference and lower-limb acceleration data","volume":"27","author":"Naghavi","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref69","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1109\/JBHI.2021.3103071","article-title":"Towards real-time prediction of freezing of gait in patients with Parkinson's disease: a novel deep one-class classifier","volume":"26","author":"Naghavi","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref70","author":"O'Day","year":"2020"},{"key":"ref71","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s12984-022-00992-x","article-title":"Assessing inertial measurement unit locations for freezing of gait detection and patient preference","volume":"19","author":"O'Day","year":"2022","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref72","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3389\/fneur.2017.00394","article-title":"Identification of characteristic motor patterns preceding freezing of gait in Parkinson's disease using wearable sensors","volume":"8","author":"Palmerini","year":"2017","journal-title":"Front. Neurol."},{"key":"ref73","doi-asserted-by":"publisher","first-page":"831063","DOI":"10.3389\/fneur.2022.831063","article-title":"Prediction of freezing of gait in Parkinson's disease using unilateral and bilateral plantar-pressure data","volume":"13","author":"Pardoel","year":"2022","journal-title":"Front. Neurol."},{"key":"ref74","doi-asserted-by":"publisher","first-page":"e0258544","DOI":"10.1371\/journal.pone.0258544","article-title":"Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease","volume":"16","author":"Pardoel","year":"","journal-title":"PLoS One"},{"key":"ref75","doi-asserted-by":"publisher","first-page":"2246","DOI":"10.3390\/s21062246","article-title":"Early detection of freezing of gait during walking using inertial measurement unit and plantar pressure distribution data","volume":"21","author":"Pardoel","year":"","journal-title":"Sensors"},{"key":"ref76","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1097\/WCO.0000000000000862","article-title":"Cognitive and behavioural impairment in amyotrophic lateral sclerosis","volume":"33","author":"Pender","year":"2020","journal-title":"Curr. Opin. Neurol."},{"key":"ref77","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1109\/TBME.2017.2665438","article-title":"Freezing of gait detection in Parkinson's disease: a subject-independent detector using anomaly scores","volume":"64","author":"Pham","year":"2017","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"ref78","doi-asserted-by":"publisher","first-page":"11612","DOI":"10.1109\/jsen.2019.2932584","article-title":"A smart inertial system for 24h monitoring and classification of tremor and freezing of gait in Parkinson's disease","volume":"19","author":"Pierleoni","year":"2019","journal-title":"IEEE Sensors J."},{"key":"ref79","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1109\/tmrb.2021.3091526","article-title":"Continuous identification of freezing of gait in Parkinsons patients using artificial neural networks and instrumented shoes","volume":"3","author":"Prado","year":"2021","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref80","doi-asserted-by":"publisher","first-page":"2152","DOI":"10.1109\/TBME.2017.2785625","article-title":"Modeling, detecting, and tracking freezing of gait in Parkinson disease using inertial sensors","volume":"65","author":"Prateek","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref81","doi-asserted-by":"publisher","first-page":"4474","DOI":"10.3390\/s20164474","article-title":"Using wearable sensors and machine learning to automatically detect freezing of gait during a FOG-provoking test","volume":"20","author":"Reches","year":"2020","journal-title":"Sensors"},{"key":"ref82","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1186\/s12883-022-02732-z","article-title":"Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors","volume":"22","author":"Ren","year":"2022","journal-title":"BMC Neurol."},{"key":"ref840","doi-asserted-by":"publisher","first-page":"832463","DOI":"10.3389\/fnins.2022.832463","article-title":"A Public Data Set of Videos, Inertial Measurement Unit, and Clinical Scales of Freezing of Gait in Individuals With Parkinson\u2019s Disease During a Turning-In-Place Task","volume":"16","author":"Ribeiro De Souza","year":"2022","journal-title":"Front Neurosci."},{"key":"ref83","doi-asserted-by":"publisher","first-page":"475","DOI":"10.3390\/s16040475","article-title":"Towards real-time detection of freezing of gait using wavelet transform on wireless accelerometer data","volume":"16","author":"Rezvanian","year":"2016","journal-title":"Sensors"},{"key":"ref84","doi-asserted-by":"publisher","first-page":"e0171764","DOI":"10.1371\/journal.pone.0171764","article-title":"Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer","volume":"12","author":"Rodr\u00edguez-Mart\u00edn","year":"2017","journal-title":"PLoS One"},{"key":"ref85","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1007\/s13042-015-0480-0","article-title":"Detection of freezing of gait for Parkinson's disease patients with multi-sensor device and Gaussian neural networks","volume":"8","author":"Saad","year":"2017","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref86","first-page":"135","article-title":"Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments","author":"Sama","year":"2017"},{"key":"ref87","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3390\/electronics8020119","article-title":"Increasing robustness in the detection of freezing of gait in Parkinson's disease","volume":"8","author":"San-Segundo","year":"2019","journal-title":"Electronics"},{"key":"ref88","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1046\/j.1468-1331.2003.00611.x","article-title":"Characterization of freezing of gait subtypes and the response of each to levodopa in Parkinson's disease","volume":"10","author":"Schaafsma","year":"2003","journal-title":"Eur. J. Neurol."},{"key":"ref89","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3233\/NRE-182531","article-title":"Correlation of falls in patients with amyotrophic lateral sclerosis with objective measures of balance, strength, and spasticity","volume":"44","author":"Schell","year":"2019","journal-title":"NeuroRehabilitation"},{"key":"ref90","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1186\/s12984-021-00958-5","article-title":"Prediction and detection of freezing of gait in Parkinson's disease from plantar pressure data using long short-term memory neural-networks","volume":"18","author":"Shalin","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref91","doi-asserted-by":"publisher","first-page":"2256","DOI":"10.1109\/TBME.2022.3140258","article-title":"Detection of freezing of gait using convolutional neural networks and data from lower limb motion sensors","volume":"69","author":"Shi","year":"2022","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"ref92","first-page":"5410","author":"Shi","year":"2020"},{"key":"ref93","doi-asserted-by":"publisher","first-page":"105482","DOI":"10.1016\/j.engappai.2022.105482","article-title":"Improvement of performance in freezing of gait detection in Parkinson's disease using transformer networks and a single waist-worn triaxial accelerometer","volume":"116","author":"Sigcha","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref94","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.3390\/s20071895","article-title":"Deep learning approaches for detecting freezing of gait in Parkinson's disease patients through on-body acceleration sensors","volume":"20","author":"Sigcha","year":"2020","journal-title":"Sensors (Basel, Switzerland)"},{"key":"ref95","doi-asserted-by":"publisher","first-page":"7900","DOI":"10.1109\/IEMBS.2011.6091948","article-title":"Gait feature extraction in parkinson's disease using low-cost accelerometers","volume":"2011","author":"Stamatakis","year":"2011","journal-title":"Annu Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref96","doi-asserted-by":"publisher","first-page":"e14","DOI":"10.2196\/mhealth.2539","article-title":"Position and orientation tracking in a ubiquitous monitoring system for Parkinson disease patients with freezing of gait symptom","volume":"1","author":"Takac","year":"2013","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref97","first-page":"7165","author":"Tamura","year":"2005"},{"key":"ref98","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1007\/s40846-020-00551-6","article-title":"Feasibility of smartphone-based gait assessment for Parkinson's disease","volume":"40","author":"Tang","year":"2020","journal-title":"J. Med. Biol. Eng."},{"key":"ref99","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.cmpb.2012.10.016","article-title":"Automatic detection of freezing of gait events in patients with Parkinson's disease","volume":"110","author":"Tripoliti","year":"2013","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref100","doi-asserted-by":"publisher","first-page":"21329","DOI":"10.3390\/s141121329","article-title":"PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease","volume":"14","author":"Tzallas","year":"2014","journal-title":"Sensors"},{"key":"ref101","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1109\/JBHI.2022.3215921","article-title":"Fall risk prediction in Parkinson\u2019s disease using real-world inertial sensor gait data","volume":"27","author":"Ullrich","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref102","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1166\/jmihi.2018.2379","article-title":"A machine learning approach to detecting of freezing of gait in parkinson's disease patients","volume":"8","author":"Xia","year":"2018","journal-title":"J. Med. Imaging Health Informat."},{"key":"ref103","doi-asserted-by":"publisher","first-page":"606427","DOI":"10.1155\/2014\/606427","article-title":"Temporal characteristics of high-frequency lower-limb oscillation during freezing of gait in Parkinson's disease","volume":"2014","author":"Yungher","year":"2014","journal-title":"Parkinson's Dis."},{"key":"ref104","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1016\/j.parkreldis.2015.09.051","article-title":"Identifying freezing of gait in Parkinson's disease during freezing provoking tasks using waist-mounted accelerometry","volume":"21","author":"Zach","year":"2015","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref105","author":"Zia","year":"2020"},{"key":"ref106","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2021.3063765","article-title":"Detection of generalized tonic clonic seizures and falls in unconstraint environment using smartphone accelerometer","volume":"99","author":"Zia","year":"2021","journal-title":"IEEE Access"}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2026.1766109\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:47:38Z","timestamp":1773384458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2026.1766109\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,13]]},"references-count":107,"alternative-id":["10.3389\/fnbot.2026.1766109"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2026.1766109","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,13]]},"article-number":"1766109"}}