Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients
Open accessAbstract
BACKGROUND: Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients' mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor. METHODS: Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH). RESULTS: The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation. CONCLUSION: The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In study [48], the Hierarchical Fuzzy Inference System achieved 70.3% accuracy.
“70.3%”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [48]
The Results (Overall performance, Table 6; page not available) report a 90.4% correct classification rate for H-FIS activity recognition, not 70.3%. Table 7 reports 98.2% for classification of activity levels. A search for 70.3% found no matching result in our copy. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredIn activity recognition, researchers also covered kitchen tasks and routine activities such as making a drink, chopping food, making the bed, reading, lacing shoes, folding, sweeping, and brushing teeth.
“performing kitchen tasks like making a drink, chopping food [42] and other routine activities like making the bed, reading and lacing shoes [48], folding, sweeping and brushing teeth [46,48,49].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [48]
The Data collection section (page unavailable) lists reading the newspaper and shoe lacing, and also pouring and drinking water. It does not list making the bed, folding, sweeping, or brushing teeth. In the citing sentence, the kitchen-task citation is [42], so those tasks need not be supported by this source. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDesigning efficient ML-based assessment systems that generalise to new users requires a large number of participants with different and variant degrees of severity.
“In addition, in order to design efficient ML-based assessment systems that generalises to new users a large number of participants should take part with different and variant degrees of severity [43,48,49,69,70], which is not always available and taken into account.”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [48]
The Discussion identifies the small sample as a limitation and proposes validation with more stroke patients. It also discusses the risk that machine-learning activity classifiers may not perform similarly in real-life settings. It does not say that participants spanning different degrees of severity are required for generalisation to new users. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 11,966 · see citing works
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417 · see citing works
An experiment in linguistic synthesis with a fuzzy logic controller
Mamdani, E. H., & ASSILIAN, S. (1975) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(75)80002-2 · Cited by 5,795 · see citing works
An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller
Mamdani, E. H., & ASSILIAN, S. (1999) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.1973.0303 · Cited by 5,627 · see citing works
Feigin, V. L., Forouzanfar, M. H., Krishnamurthi, R., Mensah, G. A., Connor, M., Bennett, D. A., Moran, A. E., Sacco, R. L., Anderson, L., Truelsen, T., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)61953-4 · Cited by 3,899 · see citing works
Theoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025 · cited 1× · see citing works
“To avoid over-fitting, the feature set was reduced using ReliefF algorithm [35] to K features to form the minimal feature set Ωepochs = {Nepoch × K features}.”
MethodA Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
Friedman, M. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731944 · Cited by 2,785 · see citing works
Usefulness of the Berg Balance Scale in Stroke Rehabilitation: A Systematic Review
Blum, L. M., & Korner-Bitensky, N. A. (2008) · Physical Therapy · doi:10.2522/ptj.20070205 · Cited by 1,211 · see citing works
A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring
Yang, C.-C., & Hsu, Y.-L. (2010) · Sensors · doi:10.3390/s100807772 · Cited by 1,058 · see citing works
Development of a Stroke-Specific Quality of Life Scale
Williams, L. S., Weinberger, M., Harris, L. E., Clark, D. O., & Biller, J. (1999) · Stroke · doi:10.1161/01.str.30.7.1362 · Cited by 914 · see citing works
Duncan, P. W., Bode, R. K., Lai, S. M., & Perera, S. (2003) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/s0003-9993(03)00035-2 · Cited by 913 · see citing works
Application of majority voting to pattern recognition: an analysis of its behavior and performance
Lam, L., & Suen, S.-P. (1997) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/3468.618255 · Cited by 870 · see citing works
Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Büla, C., & Robert, P. A. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.812189 · Cited by 760 · see citing works
Self-citationGrant, P., Ryan, C. G., Tigbe, W. W., & Granat, M. H. (2006) · British Journal of Sports Medicine · doi:10.1136/bjsm.2006.030262 · Cited by 683 · see citing works
Gait Assessment in Parkinson's Disease: Toward an Ambulatory System for Long-Term Monitoring
Salarian, A., Russmann, H., Vingerhoets, F. J. G., Dehollain, C., Blanc, Y., Burkhard, P. R., & Aminian, K. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827933 · Cited by 658 · see citing works
Self-citationClassification and Regression Trees
Jones, E., Harden, S., & Crawley, M. J. (2022) · doi:10.1002/9781119634461.ch20 · Cited by 505 · see citing works
iTUG, a Sensitive and Reliable Measure of Mobility
Salarian, A., Horak, F. B., Zampieri, C., Carlson-Kuhta, P., Nutt, J. G., & Aminian, K. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2010.2047606 · Cited by 489 · see citing works
Self-citationA Practical Approach to Recognizing Physical Activities
Lester, J., Choudhury, T., & Borriello, G. (2006) · Lecture notes in computer science · doi:10.1007/11748625_1 · Cited by 464 · see citing works
Najafi, B., Aminian, K., Loew, F., Blanc, Y., & Robert, P. A. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2002.800763 · Cited by 435 · see citing works
Self-citationRaju, G. V. S., Jun, Z., & Kisner, R. A. (1991) · International Journal of Control · doi:10.1080/00207179108934205 · Cited by 412 · see citing works
Methods for objective measure, quantification and analysis of sedentary behaviour and inactivity
Chastin, S. F. M., & Granat, M. H. (2010) · Gait & Posture · doi:10.1016/j.gaitpost.2009.09.002 · Cited by 323 · see citing works
How humans walk: Bout duration, steps per bout, and rest duration
Orendurff, M. S. (2008) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2007.11.0197 · Cited by 306 · see citing works
A Fortran subroutine for solving systems of non-linear algebraic equations
Powell, M. (1968) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 222 · see citing works
Ambulatory monitoring of physical activities in patients with Parkinson’s disease
Salarian, A., Russmann, H., Vingerhoets, F. J. G., Burkhard, P. R., & Aminian, K. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.896591 · Cited by 217 · see citing works
Self-citationGyllensten, I. C., & Bonomi, A. G. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2160723 · Cited by 183 · see citing works
Quaternion-based fusion of gyroscopes and accelerometers to improve 3D angle measurement
Favre, J., Jolles, B. M., Siegrist, O., & Aminian, K. (2006) · Electronics Letters · doi:10.1049/el:20060124 · Cited by 159 · see citing works
Self-citationBerberan-Santos, M. N., Bodunov, E. N., & Pogliani, L. (1997) · American Journal of Physics · doi:10.1119/1.18555 · Cited by 150 · see citing works
Activity classification using a single chest mounted tri-axial accelerometer
Godfrey, A., Bourke, A. K., OLaighin, G. M., et al. (2011) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2011.05.002 · Cited by 144 · see citing works
Ability of Thigh-Worn ActiGraph and activPAL Monitors to Classify Posture and Motion
Steeves, J. A., Bowles, H. R., McClain, J. J., Dodd, K. W., Brychta, R. J., WANG, J., & Chen, K. Y. (2014) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000497 · Cited by 130 · see citing works
Moncada‐Torres, A., Leuenberger, K., Gonzenbach, R. R., Luft, A. R., & Gassert, R. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/7/1245 · Cited by 113 · see citing works
Self-citationMulti-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people
Ganea, R., Paraschiv-Ionescu, A., Büla, C., Rochat, S., & Aminian, K. (2011) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2011.04.015 · Cited by 111 · see citing works
Self-citationParaschiv-Ionescu, A., Buchser, E., Rutschmann, B., et al. (2003) · Gait & Posture · doi:10.1016/j.gaitpost.2003.07.005 · Cited by 99 · see citing works
Self-citationAlzahrani, M. A., Ada, L., & Dean, C. M. (2011) · Journal of physiotherapy · doi:10.1016/s1836-9553(11)70007-8 · Cited by 74 · see citing works
Detection and Classification of Postural Transitions in Real-World Conditions
Ganea, R., Paraschiv-lonescu, A., & Aminian, K. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2202691 · Cited by 60 · see citing works
Self-citationLindemann, U., Zijlstra, W., Aminian, K., CHASTIN, S. F. M., de Bruin, E. D., Helbostad, J. L., & Bussmann, J. B. J. (2014) · Sensors · doi:10.3390/s140101267 · Cited by 60 · see citing works
Self-citationIdentifying Activity Levels and Steps of People With Stroke Using a Novel Shoe-Based Sensor
Fulk, G. D., Edgar, S., Bierwirth, R., Hart, P., Lopez‐Meyer, P., & Sazonov, E. E. (2012) · Neurology Report · doi:10.1097/npt.0b013e318256370c · Cited by 51 · see citing works
Impact of study design on development and evaluation of an activity-type classifier
van Hees, V. T., Golubić, R., Ekelund, U., & Brage, S. (2013) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00984.2012 · Cited by 49 · see citing works
Designing a hierarchical fuzzy logic controller using the differential evolution approach
Cheong, C., & Lai, R. (2006) · Applied Soft Computing · doi:10.1016/j.asoc.2006.12.001 · Cited by 48 · see citing works
Novak, A. C., & Brouwer, B. J. (2012) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2011.10.009 · Cited by 47 · see citing works
A kinematic analysis of recovery of the ability to stand up following stroke
Ada, L., & Westwood, P. (1992) · Australian Journal of Physiotherapy · doi:10.1016/s0004-9514(14)60558-4 · Cited by 36 · see citing works
Schasfoort, F. C., Bussmann, J. B. J., Martens, W. L. J., & Stam, H. J. (2006) · Behavior Research Methods · doi:10.3758/bf03192798 · Cited by 16 · see citing works
Solar Photodetachment Rates of Monatomic Anions
Helmy, E. M., & Woo, S. (1985) · Australian Journal of Physics · doi:10.1071/ph850135 · Cited by 7 · see citing works
Physical activity of moderately impaired elderly stroke patients during rehabilitation
Lindemann, U., Jamour, M., Nicolai, S., Benzinger, P., Klenk, J., Aminian, K., & Becker, C. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/11/1923 · Cited by 6 · see citing works
Self-citationThe Sit-to-Stand Movement recovery after stroke and objective assessment
Janssen, W. G. M. (2008) · RePub (Erasmus University Rotterdam) · Cited by 4 · see citing works
Massé, F., Gonzenbach, R. R., Ionescu, A., et al. (2014) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [37]Labatut V, Cherifi H. Accuracy measures for the comparison of classifiers, presented at the The 5th International Conference on Information Technology, Amman, Jordanie. 2012.
Cited by
Sorted by citations
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“Other wearable sensors used in HAR include (but are not limited to) piezoelectric sensor [54, 146] for converting changes in pressure, acceleration, temperature, strain, or force to electrical charge, barometric pressure sensor [183] for atmospheric pressure, temperature measurement [32], electroencephalography (EEG)…”
MethodWearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review
Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., & McDonald-Maier, K. D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103197 · Cited by 180 · see citing works
“Other researchers covered activities for specific body parts like recognising different hand gestures [41], arm gestures [43] and some exercises to strengthen shoulders, and arms [48].”
BackgroundMethodResultInfluential
- Authors
- Massé, F., Gonzenbach, R. R., Arami, A., Paraschiv-Ionescu, A., Luft, A. R., & Aminian, K.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- University Hospital Zurich (CH)
- Corresponding author
- Aminian, Kamiar
- ORCID
- Venue
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2015-08-25
- Volume
- 12
- Issue
- 1
- Pages
- 72-72
- ISSN
- 1743-0003 (ISSN-L)
- PMC
- PMC4549072
- OpenAlex
- W1911839968
- Cited by
- 77
- References
- 48
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 4 · 2023: 10 · 2022: 12 · 2021: 4 · 2020: 14 · 2019: 7 · 2018: 9 · 2017: 3 · 2016: 6 · 2015: 1
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- European Commission (287713)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- stroke, activity recognition, barometric pressure sensor, inertial sensors, mobility impairment, postural transitions, fuzzy inference system, non-intrusive monitoring, daily activity monitoring
- MeSH terms
- Stroke Rehabilitation, Aged, Algorithms, Biomechanical Phenomena, Female, Humans, Locomotion, Male, Middle Aged, Motor Activity, Movement, Posture, Pressure, Reproducibility of Results, Walking, Fuzzy Logic, Monitoring, Ambulatory, Mobility Limitation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
