Wearable Device to Monitor Back Movements Using an Inductive Textile Sensor

Abstract

Low back pain (LBP) is the most common work-related musculoskeletal disorder among healthcare workers and is directly related to long hours of working in twisted/bent postures or with awkward trunk movements. It has already been established that providing relevant feedback helps individuals to maintain better body posture during the activities of daily living. With the goal of preventing LBP through objective monitoring of back posture, this paper proposes a wireless, comfortable, and compact textile-based wearable platform to track trunk movements when the user bends forward. The smart garment developed for this purpose was prototyped with an inductive sensor formed by sewing a copper wire into an elastic fabric in a zigzag pattern. The results of an extensive simulation study showed that this unique design increases the inductance value of the sensor, and, consequently, improves its resolution. Furthermore, experimental evaluation on a healthy participant confirmed that the proposed wearable system with the suggested sensor design can easily detect forward bending movements. The evaluation scenario was then extended to also include twisting and lateral bending of the trunk, and it was observed that the proposed design can successfully discriminate such movements from forward bending of the trunk. Results of the magnetic interference test showed that, most notably, moving a cellphone towards the unworn prototype affects sensor readings, however, manipulating a cellphone, when wearing the prototype, did not affect the capability of the sensor in detecting forward bends. The proposed platform is a promising step toward developing wearable systems to monitor back posture in order to prevent or treat LBP.

Understanding the Impact of Machine Learning Models on the Performance of Different Flexible Strain Sensor Modalities

“…The susceptibility of inductive sensors to noise was observed with the inductive sensor having the lowest SNR in Table 2 and a higher noise floor in Figure 6A. The issue of noise susceptibility with inductive and even un-shielded capacitive sensors have been reported (Yang et al, 2017;Patiño et al, 2020). A summary of qualitative factors relating to the practicality of using each sensor type is shown in Table 3.…”

Detection of sitting posture using hierarchical image composition and deep learning

“…For identifying inadequate posture wearable textile sensors ( García Patiño, Khoshnam & Menon, 2020 ), inertial and magnetic sensors attached to human body ( Bouvier et al, 2015 ), depth cameras ( Ho et al, 2016 ), radio-frequency identification tags ( Saab & Msheik, 2016 ), 3D motion analysis ( Perusquía-Hernández et al, 2017 ), video surveillance ( Afza et al, 2021 ), Kinect sensors ( Ryselis et al, 2020 ) and sensors attached to office chairs ( Zemp et al, 2016 ; Bibbo et al, 2019 ) were used, while registering body posture parameters such as forward inclination, distance to the computer, and relative skeletal angles. However, the camera-based systems have demanding requirements for distance, proper lighting, calibration and non-occlusion.…”

Wearable Textile UHF-RFID Sensors: A Systematic Review

“…Currently, metal materials [46], such as copper, aluminum, iron, silver and gold, and synthetic fiber such as graphite fiber [47][48][49] and glass fiber, are popular in the UHF-RFID antennas and sensors designs. Note that metal materials still have better conductive ability than that of some synthetic fiber, thus some synthetic fiber is dealt with metal surface [50] or twisted by a proportional metal-plated fiber [51,52]. Certainly, compared with metal materials, synthetic fiber has the greatest strength of designability.…”