# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 7 Conclusions  
This paper has extensively reviewed the existing research articles, compared them systematically, and classified current findings taxonomically in the field of IoHT technology by analyzing several articles within the last decade (2010-2022). The paper focused on monitoring and identifying physiological parameters (vital signs) acquired using wearable sensors (IoT) and AI for clinical health-related findings.  
The paper started with a general overview of AI, IoT, vital signs, and related experimental research works. According to the review findings, vital signs commonly monitored and measured in clinical environments include heart rate, blood pressure, respiratory rate, oxygen saturation, body temperature, and glucose level. These physiological parameters are measured based on traditional approaches, mainly heuristics, and sometimes have a high level of errors.  
Another contribution of the study constitutes the application of the IoT in healthcare, including monitoring physiological parameters, rehabilitation systems, skin pathologies, diabetes, and epidemic disease treatment. Since data are mostly acquired using smart health objects (SHO) and transit to the personal health record layer (storage), there is a need for better intelligent algorithms for enhanced analysis and interpretation of results for health given and healthcare providers. Vital signs are collected via wearable (SHO) and transmitted clinical information to the cloud via communication protocols on devices such as WiFi, WiMax, ZigBee, Bluetooth, NFC, RFID, or UWB, as summarized in Section 4.2.  
In addition, the commonly used AI techniques in vital health signs analysis such as the ML, DL, and RL algorithms were discussed. Finally, application areas of AI models in vital health signs monitoring were discussed, starting with diagnosis, prognosis and spread control, assistive systems, prediction, heart disease diagnosis, robot surgery, personalized healthcare, and monitoring.  
Conceptualization, S.B.J., A.A.L., A.O.B., M.A. and A.N.S.; data curation, A.O.B., G.K., Y.A.S., S.B. and A.G.; formal analysis., A.O.B., A.M., Y.T.M., Y.S. and B.A.A.; funding acquisition, S.B.J., A.A.I. and S.B.; investigation, S.B.J., A.A.I., A.O.B., M.A. and A.N.S.; methodology, S.B.J., A.A.I., A.O.B., M.A. and A.N.S.; Project administration, S.B.J., A.A.A., N.A.I.K., S.B., A.M. and A.G.; resources, S.B.J., A.A.A., N.A.I.K., S.B., A.G., Y.T.M., Y.S. and B.A.A.; software, M.A., Y.A.S., A.K.A. and G.K.; supervision, A.A.I., A.A.A. and N.A.I.K.; validation, A.M., Y.T.M., G.K., A.K.A. and B.A.A.; visualization, A.A.I., A.O.B., M.A., A.K.A., Y.S. and A.N.S.; writing--original draft, A.A.I., A.O.B., M.A. and A.N.S.; writing--review & editing, S.B.J., A.K.A., S.B. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
This work was supported by Yayasan Universiti Teknologi PETRONAS (YUTP) Research Grant Scheme (YUTP-FRG/015 LC0297), and Allied Grant under University Brunei Darussalam, and Tertiary Education Trust Fund (TETFund) via National Research Fund (NRF) with Ref. No. TETF/DR&D/CE/NRF/2020/SET1/65/VOL.1.  
**Conflicts of Interest:** The authors declare no conflict of interest.