axivity accelerometer Search Results


accelerometer  (Axivity Ltd)
86
Axivity Ltd accelerometer
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+accelerometer/pmc12321071-113-25-19?v=Axivity+Ltd
Average 86 stars, based on 1 article reviews
accelerometer - by Bioz Stars, 2026-08
86/100 stars
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axis accelerometer  (Axivity Ltd)
86
Axivity Ltd axis accelerometer
Axis 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+accelerometer/pmc12309056-178-4-26?v=Axivity+Ltd
Average 86 stars, based on 1 article reviews
axis accelerometer - by Bioz Stars, 2026-08
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+accelerometer/pmc13121596-245-22-24?v=Axivity+Ltd
Average 86 stars, based on 1 article reviews
wrist accelerometer - by Bioz Stars, 2026-08
86/100 stars
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wearable tri axial accelerometer  (Axivity Ltd)
86
Axivity Ltd wearable tri axial 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.
Wearable 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+accelerometer/pmc12675370-13-76-79?v=Axivity+Ltd
Average 86 stars, based on 1 article reviews
wearable tri axial accelerometer - by Bioz Stars, 2026-08
86/100 stars
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wristworn accelerometers  (Axivity Ltd)
86
Axivity Ltd wristworn 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.
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+accelerometer/10__1158_slash_1078___0432__ccr___22___2565-92-13-15?v=Axivity+Ltd
Average 86 stars, based on 1 article reviews
wristworn accelerometers - by Bioz Stars, 2026-08
86/100 stars
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accelerometer 21  (Axivity Ltd)
86
Axivity Ltd accelerometer 21
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 21, 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+accelerometer/10__1097_slash_js9__0000000000003255-83-22-2?v=Axivity+Ltd
Average 86 stars, based on 1 article reviews
accelerometer 21 - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier
accelerometer gyroscope device  (Axivity Ltd)
86
Axivity Ltd accelerometer gyroscope device
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 Gyroscope Device, 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+accelerometer/pmc12729488-71-12-17?v=Axivity+Ltd
Average 86 stars, based on 1 article reviews
accelerometer gyroscope device - by Bioz Stars, 2026-08
86/100 stars
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ax6 accelerometer  (Axivity Ltd)
86
Axivity Ltd ax6 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.
Ax6 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+accelerometer/pm41006653-52-24-26?v=Axivity+Ltd
Average 86 stars, based on 1 article reviews
ax6 accelerometer - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier
thighworn accelerometer  (Axivity Ltd)
86
Axivity Ltd thighworn 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.
Thighworn 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+accelerometer/pm41513517-59-11-13?v=Axivity+Ltd
Average 86 stars, based on 1 article reviews
thighworn accelerometer - by Bioz Stars, 2026-08
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