## 5 Applications  
To illustrate one of potential applications of the data-collection and analytics system, Figure 8 gives an example of the use of selected HRV metrics as inputs to the S+ algorithm developed in an earlier study for early detection of respiratory infections [17]. Inputs to this S+ algorithm are standardized IBI, LF, and HF values (i.e., z-scores; see Section 3.3) calculated in 5 min windows, and the output is S+ score, which was found to be correlated with the presence of infection. As seen in the bottom panel of Figure 8, the S+ scores are elevated prior to the device user reporting symptoms of illness. Symptom scores were calculated using the daily health survey that asks participants to provide a 0-3 score for 11 symptoms (0 is no symptoms and 3 is severe symptoms).  
Figure 7: Example of metrics extracted from cleaned dataset collected from the volunteer for a period of 1 week. All values were calculated in 5 min epochs. ADF = artifact data fraction; HF = high-frequency power; IBI = interbeat interval; LF = low-frequency power; RDF = raw data fraction; RMSSD = root mean square of successive differences in IBI; SDNN = standard deviation of IBI; VDF = valid data fraction.  
Table 2 lists other applications of continuous HRV monitoring, along with references to previously published studies. The presence of correlations between HRV and phenotypical measures related to specific illnesses that these studies demonstrated can enable early detection of physiological anomalies and help guide effective interventions. Other reported applications of HRV monitoring include fitness tracking, sleep quality tracking, and fatigue assessment.  
The described data-collection and analytics system is constructed for extendibility to other sensors and algorithms. Of particular relevance, given the strong dependence of HRV metrics on the type and level of physical activity, are algorithms associated with human activity recognition, a rapidly growing field of research that aims to automatically identify and classify human motions and activities from various tracking devices [25; 26].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Application** & **HRV Metrics** & **Ref.** \\ \hline Coronary artery disease & SD1, SD2 & [27] \\ Atrial fibrillation & IBI & [28] \\  & VLF, LF, HF, TP, SDNN, RMSD, & [29] \\  & pNN50, TI, SampEn & [29] \\ Chronic obstructive pulmonary disease & SDNN, LF, HF & [30] \\ Viral respiratory infection & IBI, LF, HF, LF/HF & [17] \\ Mild traumatic brain injury & IBI, LF, HF, LF/HF & [31] \\ Epilepsy & IBI, LF, HF, LF/HF & [32] \\ Major depressive disorder & LF, HF & [33] \\ Chronic fatigue syndrome & RMSSD, SDNN, pNN50, LF, HF & [34] \\ Sleep quality assessment & RMSSD, SDNN, pNN50, LF, HF & [35] \\ Fitness tracking: aerobic & \multicolumn{1}{c}{} & \\ threshold determination & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular} \(\mathrm{HF}=high\) frequency; HRV = heart rate variability, LF = low frequency; pNN50 = percentage of adjacent intervals; RMSSD = root-mean-squared successive difference of IBIs; SampEn = sample entropy; SDNN = standard deviation of IBIs; TI = triangular index; TP = total power; VLF = very low frequency.  
\end{table}
Table 2: Examples of applications of HRV monitoring.  
Figure 8: Example of use of HRV metrics as inputs to an algorithm for detection of respiratory infections. The inputs are z-scores of IBI, LF, and HF calculated in 5 min. windows. The output is the S+ score. Symptoms scores were assigned on the basis of a survey that the study participant filled out daily.