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Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: A machine learning approach.

, , , , , , , , and . EMBC, page 3712-3715. IEEE, (2016)

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Physical activity classification meets daily life: Review on existing methodologies and open challenges., , and . EMBC, page 5050-5053. IEEE, (2015)Patterns of human activity behavior: from data to information and clinical knowledge., , , , , , , , and . UbiComp Adjunct, page 841-845. ACM, (2016)Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61-70 Years Old., , , , , , , and . Sensors, 19 (3): 449 (2019)Feature Selection for Accelerometer-Based Posture Analysis in Parkinson's Disease., , , , and . IEEE Trans. Information Technology in Biomedicine, 15 (3): 481-490 (2011)Influence of age and gender on sensor-based functional measures: A factor analysis approach., , , , and . EMBC, page 5054-5057. IEEE, (2015)Developing the FARSEEING Taxonomy of Technologies: Classification and description of technology use (including ICT) in falls prevention studies., , , , , , , , , and 1 other author(s). Journal of Biomedical Informatics, (2016)Hilbert-Huang-Based Tremor Removal to Assess Postural Properties From Accelerometers., , , and . IEEE Trans. Biomed. Engineering, 58 (6): 1752-1761 (2011)A Clinical Application of Feature Selection: Quantitative Evaluation of the Locomotor Function., , , , and . IC3K, volume 272 of Communications in Computer and Information Science, page 151-157. Springer, (2010)HABITAT: An IoT Solution for Independent Elderly., , , , , , , , , and 16 other author(s). Sensors, 19 (5): 1258 (2019)Feature Selection for the Instrumented Timed Up and Go in Parkinson's Disease., , , , and . KDIR, page 95-99. SciTePress, (2010)