Idiopathic normal pressure hydrocephalus (iNPH) is a reversible cause of gait disturbance, cognitive decline, and urinary incontinence in older adults. Early detection with shunt surgery can significantly improve symptoms.
Methods
A systematic review following PRISMA guidelines evaluated the role of portable and smartphone-based accelerometers in quantifying gait parameters in iNPH.
Results
Across 32 studies, 22 different accelerometers were used, ranging from wearable inertial sensors to smartphone applications. The Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) app was the most frequently utilized, enabling detection of pathological gait features such as shuffling, short steps, and wide-based walking. The G-Walk device was commonly used in clinical assessments, including before and after CSF tap tests. Several studies integrated accelerometry with machine-learning models, improving differentiation of iNPH from other neurological gait disorders.
Conclusion
Portable accelerometry offers an accessible, objective tool for diagnosis, monitoring, and prognostication in iNPH, with growing potential for routine clinical integration.
WangZZhangYHuFDingJWangX. Pathogenesis and pathophysiology of idiopathic normal pressure hydrocephalus. CNS Neurosci Ther. 2020;26(12):1230-1240. doi:10.1111/cns.13526
2.
ZaccariaVBacigalupoIGervasiG, et al.A systematic review on the epidemiology of normal pressure hydrocephalus. Acta Neurol Scand. 2020;141(2):101-114. doi:10.1111/ane.13182
NakajimaMYamadaSMiyajimaM, et al.Guidelines for management of idiopathic normal pressure hydrocephalus (Third Edition): endorsed by the Japanese society of normal pressure hydrocephalus. Neurol Med -Chir. 2021;61(2):63-97. doi:10.2176/nmc.st.2020-0292.61.
5.
YahalomGYekutieliZIsraeli-KornS, et al.Smartphone based timed up and go test can identify postural instability in Parkinson’s Disease. Isr Med Assoc J. 2020;22(1):37-42.
6.
HalperinJJKurlanRSchwalbJMCusimanoMDGronsethGGlossD. Practice guideline: idiopathic normal pressure hydrocephalus: response to shunting and predictors of response: report of the guideline development, dissemination, and implementation subcommittee of the American academy of neurology. Neurology. 2015;85(23):2063-2071. doi:10.1212/WNL.0000000000002193
7.
WuEMEl AhmadiehTYKafkaB, et al.Clinical outcomes of normal pressure hydrocephalus in 116 patients: objective versus subjective assessment. J Neurosurg. 2020;132(6):1757-1763. doi:10.3171/2019.1.JNS181598
8.
Shema-ShiratzkySBeerYMorAElbazA. Smartphone-based inertial sensors technology - validation of a new application to measure spatiotemporal gait metrics. Gait Posture. 2022;93:102-106. doi:10.1016/j.gaitpost.2022.01.024
9.
ShaharRTAgmonM. Gait analysis using accelerometry data from a single smartphone: agreement and consistency between a smartphone application and gold-standard gait analysis System. Sensors (Basel). 2021;21(22):7497. doi:10.3390/s21227497
10.
EllisRJNgYSZhuS, et al.A validated smartphone-based assessment of gait and gait variability in Parkinson’s Disease. PLoS One. 2015;10(10):e0141694. doi:10.1371/journal.pone.0141694
11.
SchalkampAKPeallKJHarrisonNASandorC. Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis. Nat Med. 2023;29(8):2048-2056. doi:10.1038/s41591-023-02440-2
12.
CakmakOOAkarKYoussefHSamanciMYErtanSVuralA. Comparative assessment of gait and balance in patients with Parkinson’s Disease and normal pressure hydrocephalus. Sisli Etfal Hastan Tıp Bul. 2023;57(2):232-237. doi:10.1474/semb.2023.79990
13.
KuruvithadamKMennerMTaylorWRZeilingerMNStieglitzLSchmid DanersM. Data-driven investigation of gait patterns in individuals affected by normal pressure hydrocephalus. Sensors (Basel). 2021;21(19):6451. doi:10.3390/s21196451
14.
DiasSFGrafCJehliE, et al.Gait pattern analysis in the home environment as a key factor for the reliable assessment of shunt responsiveness in patients with idiopathic normal pressure hydrocephalus. Front Neurol. 2023;14:1126298. doi:10.3389/fneur.2023.1126298
15.
PageMJMoherDBossuytPM, et al.PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160. doi:10.1136/bmj.n160
16.
BäcklundTÖhbergFJohanssonGGripHSundströmN. Novel, clinically applicable method to measure step-width during the swing phase of gait. Physiol Meas. 2020;41(6):065005. doi:10.1088/1361-6579/ab95ed
17.
AtbasEGaydeckiPCallaghanMJ. A wearable gait-analysis device for idiopathic normal-pressure hydrocephalus (INPH) monitoring. Article. Biomedical Physics and Engineering Express. 2024;10(6):2. doi:10.1088/2057-1976/ad2a1a
18.
MagniSBremmRPVerrosK, et al.Machine learning differentiation of Parkinson’s disease and normal pressure hydrocephalus using wearable sensors capturing gait impairments. medRxiv. 2025;24318198:1-36. doi:10.1101/2025.01.07.24318198.
19.
LeeWHyunJChoiSI, et al.Distinguishing pathologic gait in older adults using instrumented insoles and deep neural networks. IEEE J Biomed Health Inform. 2025;29(7):4758-4768. doi:10.1109/JBHI.2025.3549454. Article in Press.
20.
GaglaniSMooreJHaynesMRHoffbergerJBRigamontiD. Using commercial activity monitors to measure gait in patients with suspected iNPH: implications for ambulatory monitoring. Cureus. 2015;7(11):e382. doi:10.7759/cureus.382
21.
TaishakuAYamadaSIsekiC, et al.Development of a gait analysis application for assessing upper and lower limb movements to detect pathological gait. Sensors (Basel). 2024;24(19):6329. doi:10.3390/s24196329
22.
PancianiPPMiglioratiKMuratoriAGelminiMPadovaniAFontanellaM. Computerized gait analysis with inertial sensor in the management of idiopathic normal pressure hydrocephalus. Eur J Phys Rehabil Med. 2018;54(5):724-729. doi:10.23736/s1973-9087.18.04949-3
23.
RydjaJPohlPEleftheriouALundinF. Gait characteristics in idiopathic normal pressure hydrocephalus: a controlled study using an inertial sensor system. Article. PLoS One. 2025;20(2 February):e0317901. doi:10.1371/journal.pone.0317901
24.
AoyagiYYamadaSUedaS, et al.Development of smartphone application for markerless three-dimensional motion capture based on deep learning model. Sensors (Basel). 2022;22(14):5282. doi:10.3390/s22145282
25.
IsekiCHayasakaTYanagawaH, et al.Artificial intelligence distinguishes pathological gait: the analysis of markerless motion capture gait data acquired by an iOS application (TDPT-GT). Sensors (Basel). 2023;23(13):6217. doi:10.3390/s23136217
26.
IsekiCSuzukiSFukamiT, et al.Fluctuations in upper and lower body movement during walking in normal pressure hydrocephalus and Parkinson’s Disease assessed by motion capture with a smartphone application, TDPT-GT. Sensors (Basel). 2023;23(22):9263. doi:10.3390/s23229263
27.
IshikawaMYamadaSYamamotoKAoyagiY. Gait analysis in a component timed-up-and-go test using a smartphone application. Article. J Neurol Sci. 2019;398:45-49. doi:10.1016/j.jns.2019.01.023
28.
TariqKThorneLTomaAWatkinsL. ‘Watkins’ & ‘Watkins 2.0’: smart phone applications (Apps) for gait-assessment in normal pressure hydrocephalus and decompensated long-standing overt ventriculomegaly. Article. Acta Neurochir. 2024;166(1):386. doi:10.1007/s00701-024-06275-9
29.
YamadaSAoyagiYYamamotoKIshikawaM. Quantitative evaluation of gait disturbance on an instrumented timed up-and-go test. Aging Dis. 2019;10(1):23-36. doi:10.14336/AD.2018.0426
30.
YamadaSAoyagiYIshikawaMYamaguchiMYamamotoKNozakiK. Gait assessment using three-dimensional acceleration of the trunk in idiopathic normal pressure hydrocephalus. Front Aging Neurosci. 2021;13:653964. doi:10.3389/fnagi.2021.653964
31.
SprauACBasilGWEliahuK, et al.Using smartphone-based accelerometers to gauge postoperative outcomes in patients with NPH: implications for ambulatory monitoring. Surg Neurol Int. 2021;12:464. doi:10.25259/sni_112_2021
32.
YamadaSIshikawaMItouH, et al.Importance of oscillating flow of cerebrospinal fluid in idiopathic normal pressure hydrocephalus. Conference abstract. Fluids Barriers CNS. 2023;19:1. doi:10.1186/s12987-022-00397-x
33.
Jusue-TorresIBrownDAPenningtonZ, et al.Objective assessment of patients with idiopathic normal pressure hydrocephalus following ventriculoperitoneal shunt placement using activity-monitoring data: pilot study. Neurosurg Focus. 2023;54(4):E6. doi:10.3171/2023.1.Focus22640
34.
NikaidoYUrakamiHAkisueT, et al.Associations among falls, gait variability, and balance function in idiopathic normal pressure hydrocephalus. Clin Neurol Neurosurg. 2019;183:105385. doi:10.1016/j.clineuro.2019.105385
35.
NourianiAJonasonASabalLT, et al.Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients. Front Aging Neurosci. 2023;15:1117802. doi:10.3389/fnagi.2023.1117802
36.
YangFHickmanTTTinlM, et al.Quantitative evaluation of changes in gait after extended cerebrospinal fluid drainage for normal pressure hydrocephalus. J Clin Neurosci. 2016;28:31-37. doi:10.1016/j.jocn.2015.11.013
37.
FerrariAMillettiDGianniniG, et al.The effects of cerebrospinal fluid tap-test on idiopathic normal pressure hydrocephalus: An inertial sensors based assessment. J NeuroEng Rehabil. 2020;17(1):7. doi:10.1186/s12984-019-0638-1
38.
HeMQiZShaoY, et al.Quantitative evaluation of gait changes using APDM inertial sensors after the external lumbar drain in patients with idiopathic normal pressure hydrocephalus. Front Neurol. 2021;12:635044. doi:10.3389/fneur.2021.635044
39.
GagoMFFerreiraFBichoE. Quantitative gait analysis value as a predictor of shunt surgery effectiveness in normal pressure hydrocephalus: a technical note. Clin Neurol Neurosurg. 2022;221:107405. doi:10.1016/j.clineuro.2022.107405
40.
FerrariAMillettiDPalumboP, et al.Gait apraxia evaluation in normal pressure hydrocephalus using inertial sensors. Clinical correlates, ventriculoperitoneal shunt outcomes, and tap-test predictive capacity. Fluids Barriers CNS. 2022;19(1):51. doi:10.1186/s12987-022-00350-y
41.
Öztop-ÇakmakÖAkarKYoussefH, et al.Gait analysis in idiopathic normal pressure hydrocephalus: a single Centre experience. Istanbul Medical Journal. 2023;24(1):36-39. doi:10.4274/imj.galenos.2023.60251
42.
UmitYNihalGDeryaKİlkeKAhmet TuranI. The effects of a cerebrospinal fluid tap test on gait, balance and turning in elderly patients with idiopathic normal pressure hydrocephalus. Int J Gerontol. 2024;18(3):173-178. doi:10.6890/IJGE.202407_18(3).0007
43.
NikaidoYAkisueTKajimotoY, et al.The effect of CSF drainage on ambulatory center of mass movement in idiopathic normal pressure hydrocephalus. Article. Gait Posture. 2018;63:5-9. doi:10.1016/j.gaitpost.2018.04.024
44.
MostileGContrafattoFTerranovaR, et al.Turning and sitting in early parkinsonism: differences between idiopathic normal pressure hydrocephalus associated with Parkinsonism and Parkinson’s Disease. Mov Disord Clin Pract. 2023;10(3):466-471. doi:10.1002/mdc3.13638
45.
LeeWChoiS-IChungSJHwangJKKimNY. Serial assessment of gait changes after interventions using smart insole in a patient with iNPH: a proof-of-concept case report. Brain & Neuro Rehabilitation. 2025;18(1):e1. doi:10.12786/bn.2025.18.e1
46.
FerrariAMillettiDPalumboP, et al.Gait apraxia evaluation in normal pressure hydrocephalus using inertial sensors. Clinical correlates, ventriculoperitoneal shunt outcomes, and tap-test predictive capacity. Fluids Barriers CNS. 2022;19(1):51. doi:10.1186/s12987-022-00350-y
47.
PassarettiMMaranzanoABluettBRajalingamRFasanoA. Gait analysis in idiopathic normal pressure hydrocephalus: a meta-analysis. Mov Disord Clin Pract. 2023;10(11):1574-1584. doi:10.1002/mdc3.13816
48.
YamadaSAoyagiYIsekiC, et al.Quantitative gait feature assessment on two-dimensional body axis projection planes converted from three-dimensional coordinates estimated with a deep learning smartphone App. Sensors. 2023;23(2):617. doi:10.3390/s23020617
49.
LimY-HKoP-WParkK-S, et al.Quantitative gait analysis and cerebrospinal fluid tap test for idiopathic normal-pressure hydrocephalus. Sci Rep. 2019;9(1):16255. doi:10.1038/s41598-019-52448-3
50.
SchnieppRTraboldRRomagnaA, et al.Walking assessment after lumbar puncture in normal-pressure hydrocephalus: a delayed improvement over 3 days. J Neurosurg. 2017;126(1):148-157. doi:10.3171/2015.12.Jns151663
51.
PujariSKharkarSMetellusPShuckJWilliamsMARigamontiD. Normal pressure hydrocephalus: long-term outcome after shunt surgery. J Neurol Neurosurg Psychiatry. 2008;79(11):1282-1286. doi:10.1136/jnnp.2007.123620
52.
ZadkaARabinNGazitE, et al.A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders. npj Digit Med. 2024;7(1):142. doi:10.1038/s41746-024-01136-2
53.
ParkSGMunSBKimYJKimKG. Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda Equina syndrome. Sci Rep. 2025;15(1):20012. doi:10.1038/s41598-025-04065-6
54.
SarkarM. Integrating machine learning and deep learning techniques for advanced Alzheimer’s Disease detection through gait analysis. J Bus Manag Stud. 2025;7(1):140-147. doi:10.32996/jbms.2025.7.1.8
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
