In our SENDOC Northern Peripheries and Artic project (SENDoc Team, 2017), we are evaluating the effectiveness of off-the-shelf wearables for monitoring and rehabilitating remote and rural patients. Therefore, we are conducting demonstrations in 4 partner locations, where healthy participants aged over 60 years will wear a Mi Band activity tracker (Mi Global Home, 2018), a data logger and a smartphone to attain comparable data. The usability of this technology will be assessed from elders’ perspective. The data attained will then be analysed in combination with medical patient data to identify frailty. We hypothesise that off-the-shelf sensors can be used to automatically identify frailty. Statistical methods and qualitative usability questionnaires will be applied to validate or reject this hypothesis. Artificial Intelligence and machine learning methods will be employed to classify frail and pre-frail patients from non-frail patients. Cohen’s Kappa will be used to assess accuracy of classifications.
Results are not available at this stage. However, we expect that on-time therapeutic and medical advice can assist patients to recover full capacity, before frailty becomes irreversible.
|Number of pages||2|
|Publication status||Published - Sep 2018|
|Event||TMED 2018 - Translational Medicine Conference: Innovating to Live Well for Longer - The City Hotel, Derry/Londonderry, United Kingdom|
Duration: 12 Sep 2018 → 13 Sep 2018
Conference number: 9
|Conference||TMED 2018 - Translational Medicine Conference|
|Period||12/09/18 → 13/09/18|