EEG/EMG based Architecture for the Early Detection of Slip-induced Lack of Balance

In this paper, we propose the preliminary version of a novel pre-impact fall detection (PIFD) strategy, optimized for the early recognition of balance loss during the steady walking.The technique has been implemented in a multi-sensor architecture aiming to jointly analyzes the muscular and cortical activity. The physiological signals were acquired from 10 electromyography (EMC) electrodes on the lower limbs and 13 electroencephalography (EEG) sites all along the scalp.Data from the EMGs are statistically treated and used both to identify abnormal muscular activities and to trigger the cortical activity assessment. The EEG computation branch evaluate the rate of variation of the EEG power spectrum density, named m, to describe the cortical responsiveness in live bands of interest. Then, a logical conditions network allows the system to recognize the loss of balance induced by the slippage, by considering both the evaluated muscular parameters and the cortical ones.Experimental validation on six adults (supported by the motion capture system) showed that the system reacts in a time compliant with the fall dynamic request (403.16 ms), ensuring a competitive detection accuracy (Sensitivity =93.33%, Specificity=99.82 %).

[1]  Daniela De Venuto,et al.  A digital processor architecture for combined EEG/EMG falling risk prediction , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[2]  Silvestro Micera,et al.  Design and Evaluation of a new mechatronic platform for assessment and prevention of fall risks , 2011, Journal of NeuroEngineering and Rehabilitation.

[3]  Jian Liu,et al.  Automatic individual calibration in fall detection – an integrative ambulatory measurement framework , 2013, Computer methods in biomechanics and biomedical engineering.

[4]  Toshiyo Tamura,et al.  A Wearable Airbag to Prevent Fall Injuries , 2009, IEEE Transactions on Information Technology in Biomedicine.

[5]  Ling Shao,et al.  A survey on fall detection: Principles and approaches , 2013, Neurocomputing.

[6]  Mark S. Redfern,et al.  Earliest Gait Deviations During Slips: Implications For Recovery , 2013 .

[7]  Alfred C. Schouten,et al.  Cortical dynamics during preparation and execution of reactive balance responses with distinct postural demands , 2019, NeuroImage.

[8]  S. Micera,et al.  An ecologically-controlled exoskeleton can improve balance recovery after slippage , 2017, Scientific Reports.

[9]  Silvestro Micera,et al.  Pre-Impact Fall Detection: Optimal Sensor Positioning Based on a Machine Learning Paradigm , 2014, PloS one.

[10]  C. Todd,et al.  World Health Organisation Global Report on Falls Prevention in Older Age , 2007 .

[11]  B. Vellas,et al.  Prospective study of restriction of activity in old people after falls. , 1987, Age and ageing.

[12]  Xinyao Hu,et al.  Pre-impact fall detection , 2016, BioMedical Engineering OnLine.

[13]  Daniela De Venuto,et al.  Cortical reactive balance responses to unexpected slippages while walking: a pilot study , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[14]  Alberto L. Sangiovanni-Vincentelli,et al.  Designing a Cyber–Physical System for Fall Prevention by Cortico–Muscular Coupling Detection , 2016, IEEE Design & Test.

[15]  Youngho Kim,et al.  Pre-impact Fall Detection using Wearable Sensor Unit , 2014, BIODEVICES.

[16]  Daniela De Venuto,et al.  Combined EEG/EMG evaluation during a novel dual task paradigm for gait analysis , 2015, 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI).

[17]  Daniela De Venuto,et al.  FPGA based architecture for fall-risk assessment during gait monitoring by synchronous EEG/EMG , 2015, 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI).