Calibration of accelerometer output for adults.

By Charles Matthew. Medicine & Science in Sports & Exercise, 2005

706 citations recorded by the source corpus.

Subfields: Complementary and alternative medicine, Physiology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/09ee18c54530866aaa1684ded6b2e63bab7456f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Complementary and alternative medicine

1. S. Crouter
2. Patty S. Freedson
3. David Bassett
4. Kong Y. Chen
5. C. Matthews

In Physiology

1. David Bassett
2. C. Matthews
3. K. Westerterp
4. Alberto G. Bonomi
5. S. Crouter
6. Macie S. Buchowski
7. Leena Choi
8. Zhouwen Liu

In Pulmonary and Respiratory Medicine

1. David Bassett
2. Kong Y. Chen
3. C. Matthews

Citations in the corpus

• Validation of Accelerometer Wear and Nonwear Time Classification Algorithm (2011, background): “A challenge with the use of monitors is capturing total activity since activity monitors selectively record movement of the part of the body to which they are attached (19, 32).”
• An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. (2009, background): “While these approaches have been well received by the scientific community and often result in relatively small or nonsignificant mean differences between estimated and actual PAEE when applied to a large group of subjects, the individual estimation errors are often substantial (2, 16).”
• The technology of accelerometry-based activity monitors: current and future. (2005, methodology): “Furthermore, the validity of using different PA monitors to estimate the intensity categories of PA (in metabolic equivalents, or METs) in adults and children is reviewed by Matthews (32) and Freedson et al. (18).”
• Daily physical activity assessment with accelerometers: new insights and validation studies (2013, background): “For example, time spent in moderate physical activity is highly dependent on the cut-off points used, which vary between and even within accelerometers depending on the study referred to (46).”
• Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations (2017, neutral): “Cut-points [7, 27, 80] [11, 61, 79, 97, 98, 103] [24] NF [22] NF”