A Weft Knit Data Glove

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 12
• Main Authors: Ayodele, Emmanuel, Raza Zaidi, Syed Ali, Scott, Jane, Zhang, Zhiqiang, Hayajneh, Ali, Shittu, Samson, McLernon, Des
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; data glove; Data gloves; Elastomers; electromechanical modeling; Electromechanical sensors; Exoskeletons; Fabrics; Gloves; Histograms; Knitting; Machine learning; Motion capture; Optical fiber sensors; Rehabilitation; Resistance; Sensors; wearable; Weft; weft knit sensor; Yarn
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3068173

Rehabilitation of stroke survivors can be expedited by employing an exoskeleton. The exercises are designed such that both hands move in synergy. In this regard, often, motion capture data from the healthy hand is used to derive control behavior for the exoskeleton. Therefore, data gloves can provide a low-cost solution for the motion capture of the joints in the hand. However, current data gloves are bulky, inaccurate, or inconsistent. These disadvantages are inherited because the conventional design of a glove involves an external attachment that degrades overtime and causes inaccuracies. This article presents a weft knit data glove whose sensors and support structure are manufactured in the same fabrication process, thus removing the need for an external attachment. The glove is made by knitting multifilament conductive yarn and an elastomeric yarn using WholeGarment technology. Furthermore, we present a detailed electromechanical model of the sensors alongside its experimental validation. In addition, the reliability of the glove is verified experimentally. Finally, machine learning algorithms are implemented for classifying the posture of hand on the basis of sensor data histograms.