Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 781

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

Open access
Mannini, Andrea, Sabatini, Angelo Maria·2010·Sensors
DOI 10.3390/s100201154
Open this paper in CoChat — free account

Abstract

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/s100201154

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 44

  1. Meijer, 1991, Methods to assess physical activity with special reference to motion sensors and accelerometers, IEEE Trans. Biomed. Eng, 38, 221, 10.1109/10.133202 · DOI
  2. Bouten, 1997, A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity, IEEE Trans. Biomed. Eng, 44, 136, 10.1109/10.554760 · DOI · Paper page
  3. 1999, Context in problem solving: a survey, Knowl. Eng. Rev, 14, 47, 10.1017/S0269888999141018 · DOI · Paper page
  4. Yu, 2003, An adaptive shared control system for an intelligent mobility aid for the elderly, Auton. Rob, 15, 53, 10.1023/A:1024488717009 · DOI
  5. Hirata, 2007, Motion control of passive intelligent walker using servo brakes, IEEE Trans. Rob, 23, 981, 10.1109/TRO.2007.906252 · DOI
  6. Chuy, 2007, A control approach based on passive behavior to enhance user interaction, IEEE Trans. Rob, 23, 899, 10.1109/TRO.2007.907920 · DOI
  7. Alwan, 2007, Basic walker-assisted gait characteristics derived from forces and moments exerted on the walker's handles: results on normal subjects, Med. Eng. Phys, 29, 380, 10.1016/j.medengphy.2006.06.001 · DOI
  8. Sabatini, A.M., Genovese, V., and Pacchierotti, E. (2,, January September). A mobility aid for the support to walking and object transportation of people with motor impairments. Lausanne, Switzerland. · DOI
  9. Hirata, Y., Komatsuda, S., and Kosuge, K. (2008, January September). Fall prevention control of passive intelligent walker based on human model. Nice, France. · DOI
  10. Su, 2000, 3D arm movement recognition using syntactic pattern recognition, Artif. Intell. Eng, 14, 113, 10.1016/S0954-1810(99)00030-8 · DOI
  11. Begg, 2005, A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data, J. Biomech, 38, 401, 10.1016/j.jbiomech.2004.05.002 · DOI · Paper page
  12. Poppe, 2007, Vision-based human motion analysis: an overview, Comput. Vis. Image Underst, 108, 4, 10.1016/j.cviu.2006.10.016 · DOI · Paper page
  13. Welch, 2002, Motion tracking: no silver bullet, but a respectable arsenal, IEEE Comput. Graph. Appl, 22, 24, 10.1109/MCG.2002.1046626 · DOI
  14. Yazdi, 1998, Micromachined inertial sensors, Proc. IEEE, 86, 1640, 10.1109/5.704269 · DOI
  15. Begg, R., and Palaniswami, M. (2006). Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, Idea Group Pubilishing. · DOI
  16. Foerster, 1999, Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring, Comput. Hum. Behav, 15, 571, 10.1016/S0747-5632(99)00037-0 · DOI · Paper page
  17. Morris, 1973, Accelerometry—a technique for the measurement of human body movements, J. Biomech, 6, 729, 10.1016/0021-9290(73)90029-8 · DOI · Paper page
  18. Padgaonkar, 1975, Measurement of angular acceleration of a rigid body using linear accelerometers, ASME J. Appl. Mech, 42, 552, 10.1115/1.3423640 · DOI · Paper page
  19. Cappa, 2005, Numerical validation of linear accelerometer systems for the measurement of head kinematics, J. Biomech. Eng, 127, 919, 10.1115/1.2049329 · DOI
  20. Aminian, 1999, Physical activity monitoring based on accelerometry: validation and comparison with video observation, Med. Biol. Eng. Comput, 37, 304, 10.1007/BF02513304 · DOI · Paper page
  21. Aminian, 2002, Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes, J. Biomech, 35, 689, 10.1016/S0021-9290(02)00008-8 · DOI · Paper page
  22. Sabatini, 2005, Assessment of walking features from foot inertial sensing, IEEE Trans. Biomed. Eng, 52, 486, 10.1109/TBME.2004.840727 · DOI
  23. Foxlin, 2005, Pedestrian tracking with shoe-mounted inertial sensors, IEEE Comput. Graph. Appl, 25, 38, 10.1109/MCG.2005.140 · DOI · Paper page
  24. Mark, H. (2003). Handbook of Clinical Neurophysiology, Elsevier.
  25. Mathie, 2004, Classification of basic daily movements using a triaxial accelerometer, Med. Biol. Eng. Comput, 42, 679, 10.1007/BF02347551 · DOI · Paper page
  26. Rothney, 2007, An artificial neural network model of energy expenditure using nonintegrated acceleration signals, J. Appl. Physiol, 103, 1419, 10.1152/japplphysiol.00429.2007 · DOI
  27. Veltink, 1996, Detection of static and dynamic activities using uniaxial accelerometers, IEEE Trans. Rehab. Eng, 4, 375, 10.1109/86.547939 · DOI · Paper page
  28. Kiani, 1997, Computerized analysis of daily life motor activity for ambulatory monitoring, Technol. Health Care, 5, 307, 10.3233/THC-1997-5404 · DOI
  29. Bao, L., and Intille, S.S. (2004). Pervasive Computing, Springer Berlin/Heidelberg.
  30. Aminian, 1995, Estimation of speed and incline of walking using neural network, IEEE Trans. Instrum. Meas, 44, 743, 10.1109/19.387322 · DOI
  31. Jain, 2000, Statistical pattern recognition: a review, IEEE Trans. Pattern Anal. Mach. Intell, 22, 4, 10.1109/34.824819 · DOI
  32. Allen, 2006, Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models, Physiol. Meas, 27, 935, 10.1088/0967-3334/27/10/001 · DOI
  33. Sekine, 2002, Discrimination of walking patterns using wavelet-based fractal analysis, IEEE Trans. Neur. Syst. Rehab. Eng, 10, 188, 10.1109/TNSRE.2002.802879 · DOI
  34. Sekine, 2000, Classification of waist-acceleration signals in a continuous walking record, Med. Eng. Phys, 22, 285, 10.1016/S1350-4533(00)00041-2 · DOI
  35. Najafi, 2003, Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly, IEEE Trans. Biomed. Eng, 50, 711, 10.1109/TBME.2003.812189 · DOI
  36. Lee, 2002, Activity and location recognition using wearable sensors, IEEE Perv. Comput, 1, 24, 10.1109/MPRV.2002.1037719 · DOI
  37. Bussmann, 2001, Measuring daily behavior using ambulatory accelerometry: the Activity Monitor, Behav. Res. Meth. Instrum. Comp, 33, 349, 10.3758/BF03195388 · DOI · Paper page
  38. Karantonis, 2006, Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring, IEEE Trans. Informat. Technol. Biomed, 10, 156, 10.1109/TITB.2005.856864 · DOI · Paper page
  39. Yang, 1997, Human action learning via Hidden Markov Model, IEEE Trans. Syst. Man Cybern., Part A, 27, 34, 10.1109/3468.553220 · DOI
  40. Rabiner, 1989, A tutorial on Hidden Markov Models and selected applications in speech recognition, Proc. EEE, 77, 257
  41. Pylvalainen, 2005, Accelerometer based gesture recognition using continuous HMMs, Pattern Recogn. Image Anal, 1, 639, 10.1007/11492429_77 · DOI
  42. Liang, 1995, A real-time continuous alphabetic sign language to speech conversion VR system, Comput. Graph. Forum, 14, 67, 10.1111/j.1467-8659.1995.cgf143_0067.x · DOI
  43. Hannaford, 1991, Hidden Markov Model analysis of force/torque information in telemanipulation, Internat. J. Rob. Res, 10, 528, 10.1177/027836499101000508 · DOI
  44. Sabatini, 2008, Adaptive filtering algorithms enhance the accuracy of low-cost inertial/magnetic sensing in pedestrian navigation systems, Int. J. Comput. Intell. Appl, 7, 351, 10.1142/S1469026808002326 · DOI

Cited by

See all 781

Metadata

Authors
Mannini, A., & Sabatini, A. M.
Venue
Sensors
Publisher
MDPI AG
Published
2010-02-01
Volume
10
Issue
2
Pages
1154-1175
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s100201154
OpenAlex
W2127095067
Cited by
781
References
1
FWCI
34.28
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/3.0/ (vor)

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

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.