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This work presents a prototype of a wireless, flexible, self-powered sensor used to analyze head impact kinematics relevant to concussions, which are frequent in high contact sports. Two untethered, paper-thin, and flexible sensing devices with piezoelectric-like behavior are placed around the neck of a human head substitute and used to monitor stress/strain in this region during an impact. The mechanical energy exerted by an impact force –varied in locations and magnitudes– is converted to pulses of electric energy which are transmitted wirelessly to a smart device for storage and analysis. The wireless prototype system is presented using a microcontroller with an integrated Bluetooth Low Energy module. The static and dynamic characteristics of the transmitted signal are then compared to signals from accelerometers embedded in a head substitute, to map the sensor’s output to the angular velocity and acceleration during impacts. It is demonstrated that using only two sensors is enough to detect impacts coming from any direction; and that placing multiple external sensors around the neck region could provide accurate information on the dynamics of the head, during a collision, which other sensors fail to capture.
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Head motion classification using thread-based sensor and machine learning algorithm
Abstract Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A wireless circuit module consisting of impedance readout circuitry and a Bluetooth module records and transmits strain information to a computer. A data processing algorithm for motion recognition provides near real-time quantification of head position. Incoming data is filtered, normalized and divided into data segments. A set of features is extracted from each data segment and employed as input to nine classifiers including Support Vector Machine, Naive Bayes and KNN for position prediction. A testing accuracy of around 92% was achieved for a set of nine head orientations. Results indicate that this human machine interface platform is accurate, flexible, easy to use, and cost effective.
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- PAR ID:
- 10285433
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-021-81284-7
