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Titlebook: Delivering Superior Health and Wellness Management with IoT and Analytics; Nilmini Wickramasinghe,Freimut Bodendorf Book 2020 Springer Nat

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31#
發(fā)表于 2025-3-26 21:25:57 | 只看該作者
32#
發(fā)表于 2025-3-27 01:32:10 | 只看該作者
https://doi.org/10.1007/978-3-663-02184-1l disability due to neuron/nerve damage in hand/arm owing to brain injury or neurological disease. The proposed smart textile glove will act as signal conductor to healthy muscles and perform required actions (grab, lift, move, drop, etc.)
33#
發(fā)表于 2025-3-27 08:47:43 | 只看該作者
34#
發(fā)表于 2025-3-27 11:04:02 | 只看該作者
https://doi.org/10.1007/978-3-663-02184-1toring of heart rate variability (HRV). A central contribution is the calibration of HRV values to each user of the SmartCoping app. Calibration is crucial since HRV varies greatly among people and must be interpreted individually. Calibration is implemented in two variants, one which requires user
35#
發(fā)表于 2025-3-27 16:33:16 | 只看該作者
https://doi.org/10.1007/978-3-663-02184-1ss because more than 50% of the times the weight is regained after treatment ends and often requires an expensive and laborious multi-professional therapy. The aim of this study was to examine which guidelines for gamified wearables can be developed into an effective tool for weight loss and trigger
36#
發(fā)表于 2025-3-27 19:19:03 | 只看該作者
https://doi.org/10.1007/978-3-663-02184-1oviding better health and wellness to a growing aging population who also is experiencing a rapid rise in chronic and noncommunicable diseases. This chapter tries to uncover some of the mystique around precision wellness by first highlighting how it differs from related terms precision medicine and
37#
發(fā)表于 2025-3-28 01:23:40 | 只看該作者
38#
發(fā)表于 2025-3-28 02:48:05 | 只看該作者
39#
發(fā)表于 2025-3-28 07:56:38 | 只看該作者
40#
發(fā)表于 2025-3-28 14:28:37 | 只看該作者
,Bilanz — Bewertung — Ausblick,an in-depth observation of recent insights in the field, a literature review analysing contributions between the years of 2005 and 2017 is conducted. The aggregated results show the most important risk factors included in prediction models so far, evaluation metrics of both all-cause and diagnosis-s
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