Key result
Local biosensor virtualization using smartphone computational capability for processing raw biosignals, such as heart rate and respiratory rate from a single-lead ECG, is feasible.
Smartphones can be effectively used to process raw biosignals from simplified wearables, enabling local biosensor virtualization for heart rate and respiratory rate monitoring.
In the last few years, several wearables appeared in the market, for fitness and healthcare applications. Such smart devices have been proposed as a possible solution for lowering the costs of healthcare, leading to the mHealth revolution. In the typical scenario, each wearable, embedding sensors, processing units and communication modules, adopts a smartphone for data collection, data displaying, and remote communication. In this paper, authors modify this paradigm simplifying the wearables (e.g., relying only on simple analog front ends and communication interfaces) and exploiting the (relatively large) computational capability of the smartphone, not only for implementing gateway features but also for processing raw biosignals as well. Several experiments verify the feasibility of the proposed approach and demonstrate that “local” biosensor virtualization is possible, expanding possibilities of mHealth. In particular, tests have been carried out to evaluate the performance of hearth rate computation and respiratory rate virtual sensor, starting from a single-lead electrocardiogram signal.
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Crema et al. (2017) studied this question. Smartphone-based processing of raw biosignals (local biosensor virtualization) was evaluated on Performance of heart rate computation and respiratory rate virtual sensor from a single-lead ECG. Local biosensor virtualization using smartphone computational capability for processing raw biosignals, such as heart rate and respiratory rate from a single-lead ECG, is feasible.
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