PMID- 23689386
OWN - NLM
STAT- MEDLINE
DCOM- 20140326
LR  - 20220408
IS  - 1476-5624 (Electronic)
IS  - 1362-4393 (Linking)
VI  - 51
IP  - 9
DP  - 2013 Sep
TI  - Physical activity classification utilizing SenseWear activity monitor in manual 
      wheelchair users with spinal cord injury.
PG  - 705-9
LID - 10.1038/sc.2013.39 [doi]
AB  - STUDY DESIGN: Validation. OBJECTIVES: The primary aim of this study was to 
      develop and evaluate activity classification algorithms for a multisensor-based 
      SenseWear (SW) activity monitor that can recognize wheelchair-related activities 
      performed by manual wheelchair users (MWUs) with spinal cord injury (SCI). The 
      secondary aim was to evaluate how the accuracy in activity classification affects 
      the estimation of energy expenditure (EE) in MWUs with SCI. SETTING: 
      University-based laboratory. METHODS: Forty-five MWUs with SCI wore a SW on their 
      upper arm and participated in resting, wheelchair propulsion, arm-ergometery and 
      deskwork activities. The investigators annotated the start and end of each 
      activity trial while the SW collected multisensor data and a portable metabolic 
      cart collected criterion EE. Three methods including linear discriminant 
      analysis, quadratic discriminant analysis (QDA), and Naive Bayes (NB) were used 
      to develop classification algorithms for four activities based on the training 
      data set from 36 subjects. RESULTS: The classification accuracy was 96.3% for QDA 
      and 94.8% for NB when the classification algorithms were tested on the validation 
      data set from nine subjects. The average EE estimation errors using the 
      activity-specific EE prediction model were 5.3+/-21.5% and 4.6+/-22.8% when the QDA 
      and NB classification algorithms were applied, respectively, as opposed to 
      4.9+/-20.7% when 100% classification accuracy was assumed. CONCLUSION: The high 
      classification accuracy and low EE estimation errors suggest that the SW can be 
      used by researchers and clinicians to classify and estimate the EE for the four 
      activities tested in this study among MWUs with SCI.
FAU - Hiremath, S V
AU  - Hiremath SV
AD  - Department of Veterans Affairs, Human Engineering Research Laboratories, VA 
      Pittsburgh Healthcare System, Pittsburgh, PA, USA.
FAU - Ding, D
AU  - Ding D
FAU - Farringdon, J
AU  - Farringdon J
FAU - Vyas, N
AU  - Vyas N
FAU - Cooper, R A
AU  - Cooper RA
LA  - eng
GR  - H133E120005/PHS HHS/United States
GR  - H133FL10032/PHS HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20130521
PL  - England
TA  - Spinal Cord
JT  - Spinal cord
JID - 9609749
SB  - IM
MH  - Adult
MH  - Algorithms
MH  - Arm/physiology
MH  - Artificial Intelligence
MH  - Bayes Theorem
MH  - Data Interpretation, Statistical
MH  - Energy Metabolism
MH  - Ergometry
MH  - Female
MH  - Humans
MH  - Male
MH  - Monitoring, Ambulatory/*instrumentation/methods
MH  - Motor Activity/*physiology
MH  - Paraplegia/physiopathology
MH  - Predictive Value of Tests
MH  - Quadriplegia/physiopathology
MH  - Reproducibility of Results
MH  - *Spinal Cord Injuries
MH  - *Wheelchairs
EDAT- 2013/05/22 06:00
MHDA- 2014/03/29 06:00
CRDT- 2013/05/22 06:00
PHST- 2012/09/27 00:00 [received]
PHST- 2013/02/18 00:00 [revised]
PHST- 2013/03/18 00:00 [accepted]
PHST- 2013/05/22 06:00 [entrez]
PHST- 2013/05/22 06:00 [pubmed]
PHST- 2014/03/29 06:00 [medline]
AID - sc201339 [pii]
AID - 10.1038/sc.2013.39 [doi]
PST - ppublish
SO  - Spinal Cord. 2013 Sep;51(9):705-9. doi: 10.1038/sc.2013.39. Epub 2013 May 21.