Data Analytics and Technologies for Health

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Data Analytics and Technologies for Health 

The research group merges together bright engineers, medical doctors, researchers and PhD Students from various backgrounds and institutions. The group is a forge of cutting-edge ideas for eHealth, merging Biomedical, Electronic, and Computer Engineering to provide TeleHealth and Telemonitoring solutions based on minimally invasive, low-cost, and lightweight technologies. The mission is to augment Health using Artificial Intelligence in Data and Signal Processing to allow for accessible and personalized care, continuous monitoring, and at-home rehabilitation and support early diagnosis. Main works involve the study of neurodegenerative disorders, like Parkinson’s Disease and Alzheimer’s Dementia, in close cooperation with both Patients Associations and physicians. 

 

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Funded Projects

  • PERSIMMON – Personalized Sustainable Smart Patch Omnificence (2024-2028) is a 48-month European project aiming to develop personalized and biodegradable multimodal smart sensor patches for decentralized personal health monitoring.
  • OMNIA-PARK – Objective monitoring of axial symptoms in Parkinson’s disease: quantitative assessment in daily life based on the use of wearables, video sensing and artificial intelligence, (2023-2025) is a 24-month initiative funded by the Italian Ministry for Universities and Research (MUR) under the PRIN program, aiming to conduct an in-depth analysis of axial symptoms in Parkinson’s disease using multi-modal wearable sensors for physical and physiological signal analysis.
  • RBD automatic detection through HRV (2024-2025) is a 24-month project funded by Compagnia di San Paolo, aiming to develop a digital monitoring tool for detecting rem sleep behaviour (RBD) from heart rate variability signals. The system consists of a wearable device for data recording and artificial intelligence for predicting the risk of RBD.
  • RESTART – Research and innovation on future Telecommunications systems and networks, to make Italy more smart (2023-2025) is a 36-month project funded by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU. The project aims to study methodologies for developing novel sensors, sensor networks, communication protocols, signal processing and artificial intelligence that collaboratively work in extreme environments.
  • NODES – Nord Ovest Digitale E Sostenibile – Industry for health and silver economy (2022-2025) is a 36-month project funded by the EU under the NRRP of NextGenerationEU, aiming to push the local enterprises towards the development and scale up of hardware and software, which may assist clinicians in the delivery of telemedicine, telerehabilitation and in the empowerment of territorial assistance.
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Repositories on GitHub

Publications

  • Luis Sigcha, Luigi Borzì, Federica Amato, Irene Rechichi et al., Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review. Expert Systems with Applications 229A:120541.
  • Federica Amato, Giovanni Saggio, Valerio Cesarini, Gabriella Olmo et al., Machine learning- and statistical-based voice analysis of Parkinson’s disease patients: A survey. Expert Systems with Applications 219: 119651.
  • Luigi Borzì, Luis Sigcha, Daniel Rodríguez-Martín, Gabriella Olmo, Real-time detection of freezing of gait in Parkinson’s disease using multi-head convolutional neural networks and a single inertial sensor. Artificial Intelligence in Medicine 135:102459.
  • Giovanni Costantini, Valerio Cesarini, Pietro Di Leo, Federica Amato et al., Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison. Sensors 23(4): 2293.
  • Luigi Borzì, Luis Sigcha, Gabriella Olmo, Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson’s Disease. Sensors 23(9): 4426.
  • Claudia Ferraris, Gianluca Amprimo, Giuseppe Pettiti, Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications. Signals 4(3): 539-574.
  • Florenc Demrozi, Luigi Borzì, Gabriella Olmo, Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases. Electronics 12(6): 1269.
  • Gianluca Amprimo, Irene Rechichi, Claudia Ferraris, Gabriella Olmo, Objective Assessment of the Finger Tapping Task in Parkinson’s Disease and Control Subjects using Azure Kinect and Machine Learning. IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L’Aquila, Italy, pp. 640-645.
  • Giulia Masi, Gianluca Amprimo, Claudia Ferraris, Lorenzo Priano. Stress and Workload Assessment in Aviation—A Narrative Review. Sensors 23(7): 3556.
  • Giulia Masi, Gianluca Amprimo, Irene Rechichi, Claudia Ferraris et al., Electrodermal Activity in the Evaluation of Engagement for Telemedicine Applications. IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Atlanta, GA, USA, pp. 130-135.
  • Giulia Masi, Gianluca Amprimo, Lorenzo Priano, Claudia Ferraris. New Perspectives in Nonintrusive Sleep Monitoring for Neurodegenerative Diseases—A Narrative Review. Electronics 12(5): 1098.
  • Gianluca Amprimo, Irene Rechichi, Claudia Ferraris, Gabriella Olmo. Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study. Electronics 12(3): 623.
  • Sabrina Scimeca, Federica Amato, Gabriella Olmo, Francesco Asci et al., Robust and language-independent acoustic features in Parkinson’s disease. Frontiers in Neurology 14: 1198058.
  • Valerio Cesarini, Giovanni Costantini, Federica Amato, Vito Errico et al., Automatic Detection of Myotonia using a Sensory Glove with Resistive Flex Sensors and Machine Learning Techniques, IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), Brescia, Italy, pp. 194-199.
  • Federica Amato, Valerio Cesarini, Luca Pietrosanti, Giovanni Costantini et al., Hallmarks of Parkinson’s disease progression determined by temporal evolution of speech attractors in the reconstructed phase-space, IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), Brescia, Italy, pp. 270-274.
  • Luis Sigcha, Luigi Borzì, Ignacio Pavón, Nélson Costa et al., Improvement of Performance in Freezing of Gait detection in Parkinson’s Disease using Transformer networks and a single waist-worn triaxial accelerometer. Engineering Applications of Artificial Intelligence 116: 105482.
  • Federica Amato, Irene Rechichi, Luigi Borzì, Gabriella Olmo, Sleep Quality through Vocal Analysis: a Telemedicine Application. IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Pisa, Italy, pp. 706-711.
  • Irene Rechichi, Federica Amato, Alessandro Cicolin, Gabriella Olmo, Single-Channel EEG Detection of REM Sleep Behaviour Disorder: The Influence of REM and Slow Wave Sleep. Lecture Notes in Computer Science 13346.
  • Luis Sigcha, Beatriz Domínguez, Luigi Borzì, Nélson Costa et al., Bradykinesia Detection in Parkinson’s Disease Using Smartwatches’ Inertial Sensors and Deep Learning Methods. Electronics 11(23): 3879.
  • Federica Amato, Maria Fasani, Glauco Raffaelli, Valerio Cesarini et al., Obesity and Gastro-Esophageal Reflux voice disorders: a Machine Learning approach. IEEE International Symposium on Medical Measurements and Applications (MeMeA), Messina, Italy, pp. 1-6.
  • Gabriele Imbalzano, Domiziana Rinaldi, Giovanna Calandra-Buonaura, Manuela Contin et al., How resistant are levodopa-resistant axial symptoms? Response of freezing, posture, and voice to increasing levodopa intestinal infusion rates in Parkinson disease. European Journal of Neurology 30: 96-106.
  • Luigi Borzì, Alessandro Manoni, Alessandro Zampogna, Fernanda Irrera et al., Correlation between wearable inertial sensor data and standardised Parkinson’s disease axial impairment measures using machine learning, IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy, pp. 732-736.
  • Veronica Cimolin, Luca Vismara, Claudia Ferraris, Gianluca Amprimo et al., Computation of Gait Parameters in Post Stroke and Parkinson’s Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems. Sensors 22(3): 824.
  • Serena Cerfoglio, Claudia Ferraris, Luca Vismara, Gianluca Amprimo et al., Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review. Sensors 22(13): 4910.
  • Luca Mesin, Paola Porcu, Debora Russu, Gabriele Farina et al., A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease. Sensors 22(7): 2613.
  • Claudia Ferraris, Gianluca Amprimo, Giuseppe Pettiti, Giulia Masi et al., Automatic Detector of Gait Alterations using RGB-D sensor and supervised classifiers: a preliminary study. IEEE Symposium on Computers and Communications (ISCC), Rhodes, Greece, pp. 1-6.
  • Luigi Borzì, Ivan Mazzetta, Alessandro Zampogna, Antonio Suppa et al., Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor. Sensors 22(2): 412.
  • Claudia Ferraris, Giuseppe Pettiti, Gianluca Amprimo, Debora Desideri et al., Telerehabilitation of cognitive, motor and sleep disorders in neurological pathologies: the REHOME project. IEEE Symposium on Computers and Communications (ISCC), Rhodes, Greece, 2022, pp. 1-6.
  • Gianluca Amprimo, Claudia Ferraris, Giulia Masi, Giuseppe Pettiti et al., GMH-D: Combining Google MediaPipe and RGB-Depth Cameras for Hand Motor Skills Remote Assessment. IEEE International Conference on Digital Health (ICDH), Barcelona, Spain, 2022, pp. 132-141.
  • Gianluca Amprimo, Giulia Masi, Claudia Ferraris, Gabriella Olmo et al., A Preliminary Comparison between Traditional and Gamified Leg Agility Assessment in Parkinsonian Subjects. IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), Sydney, Australia, 2022, pp. 1-8.
  • Claudia Ferraris, Gianluca Amprimo, Giulia Masi, Luca Vismara et al., Evaluation of Arm Swing Features and Asymmetry during Gait in Parkinson’s Disease Using the Azure Kinect Sensor. Sensors 22(16): 6282.
  • Gianluca Amprimo, Giulia Masi, Lorenzo Priano, Corrado Azzaro et al., Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson’s Disease: An Integrated Approach Based on Azure Kinect. Sensors 22(21): 8173.
  • Claudia Ferraris, Irene Ronga, Roberto Pratola, Guido Coppo et al., Usability of the REHOME Solution for the Telerehabilitation in Neurological Diseases: Preliminary Results on Motor and Cognitive Platforms. Sensors 22(23): 9467.
  • Irene Rechichi, Maurizio Zibetti, Luigi Borzì, Gabriella Olmo et al., Single-channel EEG classification of sleep stages based on REM microstructure. Healthcare Technology Letters 8(3): 58-65.
  • Irene Rechichi, Antonella Iadarola, Maurizio Zibetti, Alessandro Cicolin et al., Assessing REM Sleep Behaviour Disorder: From Machine Learning Classification to the Definition of a Continuous Dissociation Index. International Journal of Environmental Research and Public Health 19(1): 248.
  • Federica Amato, Luigi Borzì, Gabriella Olmo, An algorithm for Parkinson’s disease speech classification based on isolated work analysis. Health Information Science and Systems 9(1): 32.
  • Federica Amato, Luigi Borzì, Gabriella Olmo, Carlo Alberto Artusi et al., Speech Impairment in Parkinson’s Disease: Acoustic Analysis of Unvoiced Consonants in Italian Native Speakers. IEEE Access 9: 166370-16638.
  • Claudia Ferraris, Veronica Cimolin, Luca Vismara, Valerio Votta et al., Monitoring of Gait Parameters in Post-Stroke Individuals: A Feasibility Study Using RGB-D Sensors. Sensors 21(17): 5945.
  • Luigi Borzì, Ivan Mazzetta, Alessandro Zampogna, Antonio Suppa et al., Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning. Sensors 21(2): 614.
  • Luigi Borzì, Gabriella Olmo, Carlo Alberto Artusi, Leonardo Lopiano, Detection of Freezing of Gait in People with Parkinson’s Disease using Smartphones. IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, pp. 625-635.
  • Luigi Borzì, Marilena varrecchia, Stefano Sibille, Gabriella Olmo et al., Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson’s Disease. IEEE Open Journal of Engineering in Medicine and Biology 1:140-147.
  • Luigi Borzì, Silvia Fornara, Federica Amato, Gabriella Olmo et al., Smartphone-Based Evaluation of Postural Stability in Parkinson’s Disease Patients During Quiet Stance. Electronics 9(6):919.
  • Luigi Borzì, Gabriella Olmo, Carlo Alberto Artusi, Margherita Fabbri et al., A new index to assess turning quality and postural stability in patients with Parkinson’s disease. Biomedical Signal Processing and Control 62:102059.
  • Luigi Borzì, Marilena Varrecchia, Gabriella Olmo, Carlo Alberto Artusi et al. Home monitoring of motor fluctuations in Parkinson’s disease patients. Journal of Reliable Intelligent Environments 5: 145–162.
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Address

Politecnico di Torino, Control and Computer Engineering Department
Corso Duca degli Abruzzi 24
10129, Torino
Italy

Contact Information

Call: +39 011 (090) 7080
Email: stefano.dicarlo@polito.it

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