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Monitoring of vital signs is critical for patient triage and management. Principal assessments of patient conditions include respiratory rate heart/pulse rate and blood oxygen saturation. However, these assessments are usually carried out with multiple sensors placed in different body locations. The aim of this paper is to identify a single location on the human anatomy whereby a single 1 cm × 1 cm non-invasive sensor could simultaneously measure heart rate (HR), blood oxygen saturation (SpO2), and respiration rate (RR), at rest and while walking. To evaluate the best anatomical location, we analytically compared eight anatomical locations for photoplethysmography (PPG) sensors simultaneously acquired by a single microprocessor at rest and while walking, with a comparison to a commercial pulse oximeter and respiration rate ground truth. Our results show that the forehead produced the most accurate results for HR and SpO2 both at rest and walking, however, it had poor RR results. The finger recorded similar results for HR and SpO2, however, it had more accurate RR results. Overall, we found the finger to be the best location for measurement of all three parameters at rest; however, no site was identified as capable of measuring all parameters while walking.

Wi-Fi positioning has found favour in environments which are traditionally challenging for GPS. The currently used method of Wi-Fi fingerprinting assumes that the devices used for training and locating perform identically. We have undertaken an experiment to determine how different devices behave in an empirical controlled test to identify the challenges and limitations which Wi-Fi fingerprinting positioning systems will face when deployed across many devices. We found that they performed significantly differently in respect to the mean reported signal strength -even those which came from the same vendor. We also found that multiple samples of the same device do not perform identically. Furthermore, it was found that certain devices were entirely unsuitable for positioning as they reported signal strength values uncorrelated with distance from the transmitter. Some other devices behaved in a way that made them poor candidates for use in fingerprinting. Temporal patterns were found in some wireless cards which suggest that filtering should be used. The tests also found that the use of 5GHz band signals had the potential to improve the accuracy of Wi-Fi location due to its higher stability compared to 2.4GHz. Ultimately however, the accuracy of Wi-Fi fingerprinting is limited due to many factors in the hardware and software design of Wi-Fi devices which affect the reported signal strength.

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Background An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods of care to meet demand, given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support their effectiveness in mental health contexts. Objective This literature review aims to identify current and potential physiological or physiologically related monitoring capabilities of the Apple Watch relevant to mental health monitoring and examine the accuracy and validation status of these measures and their implications for mental health treatment. Methods A literature review was conducted from June 2021 to July 2021 of both published and gray literature pertaining to the Apple Watch, mental health, and physiology. The literature review identified studies validating the sensor capabilities of the Apple Watch. Results A total of 5583 paper titles were identified, with 115 (2.06%) reviewed in full. Of these 115 papers, 19 (16.5%) were related to Apple Watch validation or comparison studies. Most studies showed that the Apple Watch could measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with the Apple Watch generally providing the best results compared with peers, despite overestimation. Heart rate variability measurements were found to have gaps in data but were able to detect mild mental stress. Activity monitoring with step counting showed good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial fibrillation detection showed mixed results, in part because of a high inconclusive result rate, but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature; however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep. Conclusions The results are encouraging regarding the application of the Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefits may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, a supportive health economic analysis, and concerns about personal health information remain key factors that must be addressed to enable broader uptake.