## 1 Introduction
The availability of higher-resolution data (e.g., the millisecond differences in time between each heartbeat versus average HR in beats per minute) allows researchers to engineer more insightful metrics (e.g., time-domain, frequency-domain, and non-linear HRV metrics) in order to develop new or more advanced analytical models and improve the performance of existing models. Additionally, researchers can utilize open-source algorithms for extracting physiological metrics to provide transparency and understanding that is not typically obtainable from proprietary analytics engines [10; 12; 17]. In this paper, we describe a system architecture for collecting and processing data from wearable sensors that is independent of OEM cloud services, is compatible with secure local operation, and provides access to high-resolution data. This system can be used for a variety of applications, from monitoring human performance and well-being to remote medical care. We implemented this architecture in a system that collects and processes data from Garmin Fenix series smartwatches [18]. The high-resolution data consist of a series of interbeat intervals (IBIs), which are time intervals between consecutive heart beats recorded with millisecond precision by the photoplethysmography (PPG) sensor integrated into the watch [19]. This data stream is accompanied by the count of steps [20], reported  
Figure 1: Typical architecture for the collection and processing of data collected by a smartwatch. once per minute, which provides a measure of the level of physical activity, as well as respiration rate and blood oxygen saturation (SpO\({}_{2}\)). The IBI and step count time series are the basis for the calculation of not only the average HR, but also a variety of HRV metrics [21]. HRV is a source of information regarding the operation of the autonomic nervous system, which regulates the human body's response to illness and other physical and psychological stressors [22]. The operation of the wearables system is illustrated with examples of datasets collected from volunteers monitored with Garmin Fenix series smartwatches in free-living conditions. We process the datasets using a combination of open-source techniques available from the literature and developed in-house. Among the techniques are algorithms for the identification of artifacts in the collected data, the extraction of HR and HRV metrics, and the standardization of these metrics for each individual with respect to the individual's activity level. The activity matching allows us to express cardiac metrics in the context of the user's physiological condition, thus advancing HR monitoring beyond absolute measurements toward relative assessment of one's cardiac performance. The rest of the paper is organized as follows: Section 2 describes the architecture of the system, its interfaces to communication networks, hardware requirements, and the software stack. Section 3 presents the methodology for processing the wearables data. Section 4 presents examples of datasets acquired using the system and metrics extracted from the datasets. Section 5 provides a brief review of potential applications of smartwatch-based monitoring for detecting disease and for optimizing human physical and mental performance. Section 6 presents a summary and outlook.