Fitness monitoring

Finding correlations with COR-RiSTOR Software as a Service Computing correlations with COR-RiSTOR hardware

Nowadays, epidermal electronic systems can be used to measure and monitor fitness parameters. Fitness parameters may reveal correlations which may be used to introduce a ternary classification of the fitness status: unhealthy, healed, healthy.

Using simultaneously recorded data from an electrocardiogram (ECG) and from a seismocardiogram (SCG) the relative position of the Q-peak in ECG (PEP data) and the relative position of AO and AC peaks in SCG (LVET data) can be determined. As an example, we use LVET data and PEP data from 108 Healthy, 2 Healed, and 6 Unhealthy fitness monitors. By performing classification using Weissler’s index, a binary classification of the fitness status can be introduced: unhealthy (6) and healthy (108+2). [Hesar et al., Biosensors and Bioelectronics 241 (2023) 115693]. The recognition rate amounts to RR=93,97%with unhealthy (0 correct, 6 wrong) and healthy (110 correct, 6 wrong).

Binary Classification

of electrocardiogram (ECG) and seismocardiogram (SCG) data using Weissler’s index

Recognition rate RR=93,97%

Ternary Classification

of electrocardiogram (ECG) and seismocardiogram (SCG) data using Weissler’s index and COR-RiSTOR data preprocessing algorithm

By performing classification using Weissler’s index [Hesar et al., Biosensors and Bioelectronics 241 (2023) 115693] and COR-RiSTOR data preprocessing algorithm, a ternary classification of the fitness status can be introduced: unhealthy (6), healed (2), and healthy (108). The recognition rate amounts to RR=99,14% with unhealthy (6 correct, 1 wrong), healed (2 correct, 0 wrong), and healthy (107 correct, 0 wrong).

Recognition rate RR=99,14%

Binary classification using artificial neural network and Weissler‘s index

  • No sensor data monitoring
  • Recognition of Healthy and Unhealthy
  • Cannot be fully automated

Ternary classification using COR-RiSTOR and Weissler‘s index

  • Sensor data monitoring
  • Recognition of Healthy, Healed, and Unhealthy
  • Can be fully automated

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