Evolving and explainable clinical risk assessment at the edge

Pazienza, Andrea; Anglani, R.; Fasciano, Corrado; Tatulli, Corrado; Vitulano, Felice

doi:10.1007/s12530-021-09403-3

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…However, with the large amount of data produced, finding a method to identify a meaningful relationship between data and extract the relevant information became necessary. In healthcare, user monitoring can be carried out in different ways by ensuring different levels of awareness [6][7][8] and security [9][10][11] for diagnostic and/or therapeutic purposes [12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support

et al. 2022

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Nowadays, modern technology is widespread in sports; therefore, finding an excellent approach to extracting knowledge from data is necessary. Machine Learning (ML) algorithms can be beneficial in biomechanical data management because they can handle a large amount of data. A fencing lunge represents an exciting scenario since it necessitates neuromuscular coordination, strength, and proper execution to succeed in a competition. However, to investigate and analyze a sports movement, it is necessary to understand its nature and goal and to identify the factors that affect its performance. The present work aims to define the best model to screen élite and novice fencers to develop further a tool to support athletes’ and trainers’ activity. We conducted a cross-sectional study in a fencing club to collect anthropometric and biomechanical data from élite and novice fencers. Wearable sensors were used to collect biomechanical data, including a wireless inertial system and four surface electromyographic (sEMG) probes. Four different ML algorithms were trained for each dataset, and the most accurate was further trained with hyperparameter tuning. The best Machine Learning algorithm was Multilayer Perceptron (MLP), which had 96.0% accuracy and 90% precision, recall, and F1-score when predicting class novice (0); and 93% precision, recall, and F1-score when predicting class élite (1). Interestingly, the MLP model has a slightly higher capacity to recognize élite fencers than novices; this is important to determine which training planning and execution are the best to achieve good performances.

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