The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.

In diabetes, possibly the most significant site of microvascular damage is the kidney. Due to diabetes and/or other co-morbidities, such as hypertension and age-related nephron loss, a significant number of people with diabetes suffer from kidney diseases. Improved diabetic care can reduce the prevalence of diabetic nephropathy (DN); however, innovative treatment approaches are still required. MicroRNA-21 (miR-21) is one of the most studied multipotent microRNAs (miRNAs), and it has been linked to renal fibrosis and exhibits significantly altered expression in DN. Targeting miR-21 offers an advantage in DN. Currently, miR-21 is being pharmacologically silenced through various methods, all of which are in early development. In this review, we summarize the role of miR-21 in the molecular pathogenesis of DN and several therapeutic strategies to use miR-21 as a therapeutic target in DN. The existing experimental interventions offer a way to rectify the lower miRNA levels as well as to reduce the higher levels. Synthetic miRNAs also referred to as miR-mimics, can compensate for abnormally low miRNA levels. Furthermore, strategies like oligonucleotides can be used to alter the miRNA levels. It is reasonable to target miR-21 for improved results because it directly contributes to the pathological processes of kidney diseases, including DN.

The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains.

Wearable technology is being used for tracking continuous events in various sectors of our lives. Wearables contain different types of sensors which can acquire movement data, blood pressure, blood sugar, temperature, and other physiological parameters. These parameters are recorded in the form of seamless univariate or multivariate time-series data. Very often, however, the data contains missing datum which disrupts the continuity of the data making it difficult to analyze the data. The missing part of the data needs to be imputed to make the remaining available data applicable. Choosing the proper imputation method is crucial for fruitful analysis and extracting underlined features from the data. In this context, this chapter discusses sensors associated with wearable technology which generate the time-series data, missing data in the wearables’ time-series data, and various imputation methods being used for imputing the missing data.