Development of a wearable for oximetry and patient physical activity correlation

Margarito, Daniela; González, Erika; Buendía-Roldán, Ivette; Selman, Moisés; Hautefeuille, Mathieu

doi:10.1117/12.2288841

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Piezoresistive Sensor [204] Force or pressure measurement Sweat rate sensor [205] Sweat measurement Inertial sensors [206,207] Linear and angular accelerations measurements Accelerometers [208] Acceleration measurement Shoe monitor sensor [209] Locomotion measurement ElectroCardiogram (ECG) sensors [210] Rate and regularity of the heart beats measurement Body temperature sensor [211] Skin temperature measurement Blood pressure sensor [212] Blood pressure measurement Pulse oximetry sensor [213] Oxygen saturation level in blood measurement Glucose sensor [214] Glucose rate measurement Smart phones [215,216] Several messurments Cameras [217] Recognition of activities and gestures from video sequences GPS [218,219] Human's movment Spirometer, Electrooculography (EOG), and galvanic skin [220] Physiological sensor Electroencephalogram (EEG) [221,222] Brain signals Magnetic field sensor [223] Inertial sensor…”

Section: Wearables and Sensing Technologymentioning

confidence: 99%

A Review of Physical Human Activity Recognition Chain Using Sensors

AlShorman¹,

Alshorman²,

Masadeh³

2020

IJEEI

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In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

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