Sensor placement determination for a wearable device in dual-arm manipulation tasks

Abstract

To enhance the quality of life for individuals in various sectors, such as industry, healthcare, and everyday activities, the development of a highly efficient and cost-effective wearable device is essential. This paper presents a feasibility study for a general estimation methodology for human activity assessment using a single sensor placed on the back. The methodology considers the minimal variability in human body movements when carrying different weights. Our approach involves optimizing the sensor's position through a simple pre-processing technique. This technique utilizes virtual sensor position and orientation data derived from a virtual mannequin based on real inertial measurement unit (IMU) measurements obtained during two-arm manipulation tasks in a warehouse setting. The primary contribution is the optimization of a single, non-invasive sensor placement on the back to accurately assess activities involving two-hand manipulation with low variability in movement. We identified 54 different activity classes with an accuracy of 91.77% using a hybrid two-dimensional Convolutional Neural Network (CNN) combined with a Bidirectional Long Short-Term Memory (BiLSTM) network (2D CNN-BiLSTM) model. This model does not require additional sensors that measure other physical quantities, such as electromyography activity. Identifying repetitive tasks involving significant bending, stretching, and twisting, which pose risks of musculoskeletal disorders and back pain, offers a solution for designing wearable devices. The portability and autonomy of these devices are crucial, and current sensor technology meets these needs with low size, cost, and consumption. Wearable medical devices can thus be effectively used for self-monitoring health, preventive medicine, and rehabilitation.