Polymer Optical Fiber-Based Smart Garment for Impact Identification and Balance Assessment

This paper presents a development of the smart garment instrumented with polymer optical fiber (POF) sensors to identify the impact location during a perturbation protocol for balance assessment. The sensor system installed on the smart garment consists of 30 multiplexed sensors, and each sensor is composed of a light-emitting diode (LED) coupled to a lateral cut in the POF. The sensor's responses are acquired by four photodetectors and the data processing is made by a microcontroller. Two tests were performed to characterize the system. First, forces were applied on each sensor using a reference force sensor to characterize their sensitivities. Second, several perturbations on predefined body areas were performed. A technique based on the sensors' responses is proposed to identify impacts. Finally, the proposed system was applied on a perturbation protocol to assess the human balance under an instability condition. Results of the first test showed different sensor sensitivities due to differences provoked by the manufacturing process, and the sensors were normalized. Results of the second test showed the ability of identifying the impact region by using the impact location identification technique. Furthermore, results of the perturbation protocol showed the feasibility of the proposed system in the balance assessment, in which the identified impact regions combined with the trunk angles obtained by an inertial measurement unit (IMU) improved the balance assessment. The proposed system is portable, low cost, with simple signal processing and ease of implementation, with possibility of scalability, in which can be adapted for other impact detection protocols.

Automated summary

This paper presents the development of a smart garment equipped with POF sensors to identify impact locations during a balance assessment perturbation protocol. The system was characterized through two tests, showing the ability to assess human balance under instability conditions. Results demonstrated the potential of the system to improve balance assessments by combining impact region identification with trunk angles obtained from an IMU.