A Focus on the Research Interests

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Research field #1: Bioengineering, Wireless Sensor Networks, EEG and EMG signals

Daniela De Venuto, Valerio F. Annese

 

Cyber-Physical System for Gait Analysis and Fall Prevention by Embedded EEG-EMG Computing

Brief Abstract: Development of non-invasive, wireless embedded system for gait analysis and preventing involuntary movements including fall. The system operates with synchronized and digitized data samples from 8 EMG (limbs) and 8 EEG (motor-cortex) channels. An embedded Altera Cyclone V FPGA operates the real-time signal pre-processing and the computation (resource utilization: 85.95% ALMs, 43283 ALUTs, 73.0% registers, 9.9% block memory; processing latency < 1ms). The system is able to prevent fall enabling the actuator in 168ms, i.e., better than the normal human time reaction (300ms).

Related Publications:

  1. V. F. Annese and D. De Venuto, "Gait analysis for fall prediction using EMG triggered movement related potentials," 2015 10th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS), Naples, 2015, pp. 1-6.
    doi: 10.1109/DTIS.2015.7127386
  2. V. F. Annese and D. De Venuto, "FPGA based architecture for fall-risk assessment during gait monitoring by synchronous EEG/EMG," 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), Gallipoli, 2015, pp. 116-121.
    doi: 10.1109/IWASI.2015.7184953
  3. M. de Tommaso, E. Vecchio, K. Ricci, A. Montemurno, D. De Venuto and V. F. Annese, "Combined EEG/EMG evaluation during a novel dual task paradigm for gait analysis," 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), Gallipoli, 2015, pp. 181-186.
    doi: 10.1109/IWASI.2015.7184949
  4. V. F. Annese and D. De Venuto, "Fall-risk assessment by combined movement related potentials and co-contraction index monitoring," 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), Atlanta, GA, 2015, pp. 1-4.
    doi: 10.1109/BioCAS.2015.7348366
  5. V. F. Annese and D. De Venuto, "The truth machine of involuntary movement: FPGA based cortico-muscular analysis for fall prevention," 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Abu Dhabi, 2015, pp. 553-558.
    doi: 10.1109/ISSPIT.2015.7394398
  6. D. De Venuto, V. F. Annese, M. Ruta, E. Di Sciascio and A. L. Sangiovanni Vincentelli, "Designing a Cyber–Physical System for Fall Prevention by Cortico–Muscular Coupling Detection," in IEEE Design & Test, vol. 33, no. 3, pp. 66-76, June 2016.
    doi: 10.1109/MDAT.2015.2480707
  7. V. F. Annese, M. Crepaldi, D. Demarchi and D. De Venuto, "A digital processor architecture for combined EEG/EMG falling risk prediction," 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, 2016, pp. 714-719.

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Research field #2: Bioengineering, BCI, P300

Daniela De Venuto, Valerio F. Annese, Giovanni Mezzina

 

Brain to Computer Connections: a Fast Time Domain Approach for BCI applications

bci

Brief Abstract: Development of a P300-based Brain Computer System for the remote control of a mechatronic actuator, such as wheelchair, or even a car, driven by EEG signals to be used by tetraplegic and paralytic users or just for safe drive in case of car. The EEG data are collected by 6 electrodes from the parietal-cortex area and online classified by a linear threshold classifier, basing on a suitable stage of Machine Learning (ML). The main improvement in remote device piloting by EEG, regards the approach used for the intentions recognition. In this project, the classification is based on the P300 and not just on the average of more not well identify potentials. This approach reduces the number of electrodes on the EEG helmet. The ML stage is based on a custom algorithm (t-RIDE- see Ref.1) which trains the following classification stage on the user’s “cognitive chronometry”. Tthe BCI presents a functional approach for time-domain features extraction, which reduce the amount of data to be analyzed, and then the system response times.  

P300-based Directions Recognition

Automated Braking System

 

Related Publications:

  1. D. De Venuto, V. F. Annese and G. Mezzina, "Remote Neuro-Cognitive Impairment Sensing Based on P300 Spatio-Temporal Monitoring," in IEEE Sensors Journal, vol. 16, no. 23, pp. 8348-8356, Dec.1, 2016. doi: 10.1109/JSEN.2016.2606553
  2. D. D. Venuto, V. F. Annese, G. Mezzina, M. Ruta and E. D. Sciascio, "Brain-computer interface using P300: a gaming approach for neurocognitive impairment diagnosis," 2016 IEEE International High Level Design Validation and Test Workshop (HLDVT), Santa Cruz, CA, 2016, pp. 93-99.
    doi: 10.1109/HLDVT.2016.7748261
  3. V. F. Annese, G. Mezzina and D. De Venuto, "Towards mobile health care: Neurocognitive impairment monitoring by BCI-based game," 2016 IEEE SENSORS, Orlando, FL, 2016, pp. 1-3.
    doi: 10.1109/ICSENS.2016.7808745