axivity ax3 accelerometer Search Results


accelerometer actigraph gt3x  (ActiGraph llc)
90
ActiGraph llc accelerometer actigraph gt3x
Accelerometer Actigraph Gt3x, supplied by ActiGraph llc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/actigraph+gt3x/pmc08750776-51-6-8
Average 90 stars, based on 1 article reviews
accelerometer actigraph gt3x - by Bioz Stars, 2026-09
90/100 stars
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tri axial accelerometer  (Axivity Ltd)
86
Axivity Ltd tri axial accelerometer
Tri Axial Accelerometer, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/accelerometer+axial+tri/pm35710327-129-31-33
Average 86 stars, based on 1 article reviews
tri axial accelerometer - by Bioz Stars, 2026-09
86/100 stars
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ax3 accelerometers  (Axivity Ltd)
86
Axivity Ltd ax3 accelerometers
Ax3 Accelerometers, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/accelerometer+triaxial/pmc12881634-72-15-17
Average 86 stars, based on 1 article reviews
ax3 accelerometers - by Bioz Stars, 2026-09
86/100 stars
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accelerometer geneactiv  (ActiGraph llc)
90
ActiGraph llc accelerometer geneactiv
Accelerometer Geneactiv, supplied by ActiGraph llc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/geneactiv+accelerometer/pmc10511969__heartjnl___2022___321943supp007-0-108-121
Average 90 stars, based on 1 article reviews
accelerometer geneactiv - by Bioz Stars, 2026-09
90/100 stars
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physical activity monitoring  (Axivity Ltd)
86
Axivity Ltd physical activity monitoring
Physical Activity Monitoring, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/activity+monitoring+physical/pm37950919-57-3-9
Average 86 stars, based on 1 article reviews
physical activity monitoring - by Bioz Stars, 2026-09
86/100 stars
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wristworn accelerometers  (Axivity Ltd)
86
Axivity Ltd wristworn accelerometers
Wristworn Accelerometers, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/accelerometers+wristworn/10__1158_slash_1078___0432__ccr___22___2565-92-13-15
Average 86 stars, based on 1 article reviews
wristworn accelerometers - by Bioz Stars, 2026-09
86/100 stars
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wrist accelerometer  (Axivity Ltd)
86
Axivity Ltd wrist accelerometer
Curves represent mean values (±1 standard deviation across 10 folds) obtained from nested cross-validation. Three different feature sets were compared: supervised-based accelerometry features (blue) extracted from standardized walking tests (8-ft and 32-ft walks) measuring gait speed, step length, cadence, and stride regularity; physical activity features (red) and gait quality features (green) extracted from daily-living <t>accelerometer</t> data. Classification performance, indicated by the area under the curve (AUC), improved from supervised-based features (AUC = 0.67 ± 0.09) to physical activity features (AUC = 0.69 ± 0.06), and was highest when using all available wrist multi-day gait metrics (AUC = 0.80 ± 0.06). A Friedman test showed a significant effect of feature set on AUC ( p < 0.001). Post hoc Wilcoxon tests revealed the gait quality model outperformed both supervised ( p < 0.01) and physical activity models ( p < 0.01), with no significant difference between the latter two. Note that these results are based on the application of both stages of the ElderNet pipeline: gait detection, followed by the estimation of wrist-derived gait features.
Wrist Accelerometer, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/accelerometer+wrist/pmc13121596-245-22-24
Average 86 stars, based on 1 article reviews
wrist accelerometer - by Bioz Stars, 2026-09
86/100 stars
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accelerometer actiwatch aw2  (ActiGraph llc)
90
ActiGraph llc accelerometer actiwatch aw2
Curves represent mean values (±1 standard deviation across 10 folds) obtained from nested cross-validation. Three different feature sets were compared: supervised-based accelerometry features (blue) extracted from standardized walking tests (8-ft and 32-ft walks) measuring gait speed, step length, cadence, and stride regularity; physical activity features (red) and gait quality features (green) extracted from daily-living <t>accelerometer</t> data. Classification performance, indicated by the area under the curve (AUC), improved from supervised-based features (AUC = 0.67 ± 0.09) to physical activity features (AUC = 0.69 ± 0.06), and was highest when using all available wrist multi-day gait metrics (AUC = 0.80 ± 0.06). A Friedman test showed a significant effect of feature set on AUC ( p < 0.001). Post hoc Wilcoxon tests revealed the gait quality model outperformed both supervised ( p < 0.01) and physical activity models ( p < 0.01), with no significant difference between the latter two. Note that these results are based on the application of both stages of the ElderNet pipeline: gait detection, followed by the estimation of wrist-derived gait features.
Accelerometer Actiwatch Aw2, supplied by ActiGraph llc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/accelerometer+actiwatch/pmc10511969__heartjnl___2022___321943supp007-0-15-40
Average 90 stars, based on 1 article reviews
accelerometer actiwatch aw2 - by Bioz Stars, 2026-09
90/100 stars
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accelerometer  (Axivity Ltd)
86
Axivity Ltd accelerometer
Curves represent mean values (±1 standard deviation across 10 folds) obtained from nested cross-validation. Three different feature sets were compared: supervised-based accelerometry features (blue) extracted from standardized walking tests (8-ft and 32-ft walks) measuring gait speed, step length, cadence, and stride regularity; physical activity features (red) and gait quality features (green) extracted from daily-living <t>accelerometer</t> data. Classification performance, indicated by the area under the curve (AUC), improved from supervised-based features (AUC = 0.67 ± 0.09) to physical activity features (AUC = 0.69 ± 0.06), and was highest when using all available wrist multi-day gait metrics (AUC = 0.80 ± 0.06). A Friedman test showed a significant effect of feature set on AUC ( p < 0.001). Post hoc Wilcoxon tests revealed the gait quality model outperformed both supervised ( p < 0.01) and physical activity models ( p < 0.01), with no significant difference between the latter two. Note that these results are based on the application of both stages of the ElderNet pipeline: gait detection, followed by the estimation of wrist-derived gait features.
Accelerometer, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/accelerometer/pm39729903-50-11-12
Average 86 stars, based on 1 article reviews
accelerometer - by Bioz Stars, 2026-09
86/100 stars
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substudy  (Axivity Ltd)
86
Axivity Ltd substudy
Curves represent mean values (±1 standard deviation across 10 folds) obtained from nested cross-validation. Three different feature sets were compared: supervised-based accelerometry features (blue) extracted from standardized walking tests (8-ft and 32-ft walks) measuring gait speed, step length, cadence, and stride regularity; physical activity features (red) and gait quality features (green) extracted from daily-living <t>accelerometer</t> data. Classification performance, indicated by the area under the curve (AUC), improved from supervised-based features (AUC = 0.67 ± 0.09) to physical activity features (AUC = 0.69 ± 0.06), and was highest when using all available wrist multi-day gait metrics (AUC = 0.80 ± 0.06). A Friedman test showed a significant effect of feature set on AUC ( p < 0.001). Post hoc Wilcoxon tests revealed the gait quality model outperformed both supervised ( p < 0.01) and physical activity models ( p < 0.01), with no significant difference between the latter two. Note that these results are based on the application of both stages of the ElderNet pipeline: gait detection, followed by the estimation of wrist-derived gait features.
Substudy, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/substudy/pmc12750073-120-3-9
Average 86 stars, based on 1 article reviews
substudy - by Bioz Stars, 2026-09
86/100 stars
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accelerometers ax3  (Axivity Ltd)
86
Axivity Ltd accelerometers ax3
Curves represent mean values (±1 standard deviation across 10 folds) obtained from nested cross-validation. Three different feature sets were compared: supervised-based accelerometry features (blue) extracted from standardized walking tests (8-ft and 32-ft walks) measuring gait speed, step length, cadence, and stride regularity; physical activity features (red) and gait quality features (green) extracted from daily-living <t>accelerometer</t> data. Classification performance, indicated by the area under the curve (AUC), improved from supervised-based features (AUC = 0.67 ± 0.09) to physical activity features (AUC = 0.69 ± 0.06), and was highest when using all available wrist multi-day gait metrics (AUC = 0.80 ± 0.06). A Friedman test showed a significant effect of feature set on AUC ( p < 0.001). Post hoc Wilcoxon tests revealed the gait quality model outperformed both supervised ( p < 0.01) and physical activity models ( p < 0.01), with no significant difference between the latter two. Note that these results are based on the application of both stages of the ElderNet pipeline: gait detection, followed by the estimation of wrist-derived gait features.
Accelerometers Ax3, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/used/pmc12376494-120-6-2
Average 86 stars, based on 1 article reviews
accelerometers ax3 - by Bioz Stars, 2026-09
86/100 stars
  Buy from Supplier
axivity ax3 accelerometers  (Axivity Ltd)
86
Axivity Ltd axivity ax3 accelerometers
Curves represent mean values (±1 standard deviation across 10 folds) obtained from nested cross-validation. Three different feature sets were compared: supervised-based accelerometry features (blue) extracted from standardized walking tests (8-ft and 32-ft walks) measuring gait speed, step length, cadence, and stride regularity; physical activity features (red) and gait quality features (green) extracted from daily-living <t>accelerometer</t> data. Classification performance, indicated by the area under the curve (AUC), improved from supervised-based features (AUC = 0.67 ± 0.09) to physical activity features (AUC = 0.69 ± 0.06), and was highest when using all available wrist multi-day gait metrics (AUC = 0.80 ± 0.06). A Friedman test showed a significant effect of feature set on AUC ( p < 0.001). Post hoc Wilcoxon tests revealed the gait quality model outperformed both supervised ( p < 0.01) and physical activity models ( p < 0.01), with no significant difference between the latter two. Note that these results are based on the application of both stages of the ElderNet pipeline: gait detection, followed by the estimation of wrist-derived gait features.
Axivity Ax3 Accelerometers, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/axivity+ax3+accelerometer/ax3+axivity/pm42237137-295-12-12
Average 86 stars, based on 1 article reviews
axivity ax3 accelerometers - by Bioz Stars, 2026-09
86/100 stars
  Buy from Supplier

Image Search Results


Curves represent mean values (±1 standard deviation across 10 folds) obtained from nested cross-validation. Three different feature sets were compared: supervised-based accelerometry features (blue) extracted from standardized walking tests (8-ft and 32-ft walks) measuring gait speed, step length, cadence, and stride regularity; physical activity features (red) and gait quality features (green) extracted from daily-living accelerometer data. Classification performance, indicated by the area under the curve (AUC), improved from supervised-based features (AUC = 0.67 ± 0.09) to physical activity features (AUC = 0.69 ± 0.06), and was highest when using all available wrist multi-day gait metrics (AUC = 0.80 ± 0.06). A Friedman test showed a significant effect of feature set on AUC ( p < 0.001). Post hoc Wilcoxon tests revealed the gait quality model outperformed both supervised ( p < 0.01) and physical activity models ( p < 0.01), with no significant difference between the latter two. Note that these results are based on the application of both stages of the ElderNet pipeline: gait detection, followed by the estimation of wrist-derived gait features.

Journal: NPJ Digital Medicine

Article Title: Continuous assessment of daily-living gait using self-supervised learning of wrist-worn accelerometer data

doi: 10.1038/s41746-026-02528-2

Figure Lengend Snippet: Curves represent mean values (±1 standard deviation across 10 folds) obtained from nested cross-validation. Three different feature sets were compared: supervised-based accelerometry features (blue) extracted from standardized walking tests (8-ft and 32-ft walks) measuring gait speed, step length, cadence, and stride regularity; physical activity features (red) and gait quality features (green) extracted from daily-living accelerometer data. Classification performance, indicated by the area under the curve (AUC), improved from supervised-based features (AUC = 0.67 ± 0.09) to physical activity features (AUC = 0.69 ± 0.06), and was highest when using all available wrist multi-day gait metrics (AUC = 0.80 ± 0.06). A Friedman test showed a significant effect of feature set on AUC ( p < 0.001). Post hoc Wilcoxon tests revealed the gait quality model outperformed both supervised ( p < 0.01) and physical activity models ( p < 0.01), with no significant difference between the latter two. Note that these results are based on the application of both stages of the ElderNet pipeline: gait detection, followed by the estimation of wrist-derived gait features.

Article Snippet: Dataset 2: Rush Memory and Aging Project (MAP), which includes 819 older adults (mean age 83.4 ± 7.3 years) who wore a wrist accelerometer (Axivity AX3, 50 Hz sampling rate or GENEActiv, 40 Hz sampling rate) for up to 10 consecutive days.

Techniques: Standard Deviation, Biomarker Discovery, Activity Assay, Derivative Assay