# Upper Body Posture Recognition Using Inertial Sensors and Recurrent Neural Networks
###### Abstract  
Inadequate sitting posture can cause imbalanced loading on the spine and result in abnormal spinal pressure, which serves as the main risk factor contributing to irreversible and chronic spinal deformity. Therefore, sitting posture recognition is important for understanding people's sitting behaviors and for correcting inadequate postures. Recently, wearable devices embedded with microelectromechanical systems (MEMs) sensors, such as inertial measurement units (IMUs), have received increased attention in human activity recognition. In this study, a wearable device embedded with IMUs and a machine learning algorithm were developed to classify seven static sitting postures: upright, slump, lean, right and left bending, and right and left twisting. Four 9-axis IMUs were uniformly distributed between thoracic and lumbar regions (T1-L5) and aligned on a sagittal plane to acquire kinematic information about subjects' backs during static-dynamic alternating motions. Time-domain features served as inputs to a signal-based classification model that was developed using long short-term memory-based recurrent neural network (LSTM-RNN) architecture, and the model's classification performance was used to evaluate the relevance between sensor signals and sitting postures. Overall results from performance evaluation tests indicate that this IMU-based measurement and LSTM-RNN structural scheme was appropriate for sitting posture recognition. inretial measurement units; recurrent neural network; long short-term memory; sitting posture recognition 2021  
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anteriorly and to resist tension posteriorly, protecting the spinal cord while moving or bending the body. Since the thoracic spine provides most of the stability and support for the entire trunk [1], spine deformity resulting from poor posture often occurs in this segment. Spinal deformities are abnormal alignments or curves of the vertebral column resulting from uneven loading on the contralateral side, with many contributing factors. For example, poor posture can cause acute or chronic forms of spinal deformities. Human body posture is primarily maintained by the musculoskeletal system, which includes the spine as an important component. In order to maintain body balance and stability, the relative positions of different spine segments are rearranged, resulting in changes in the spinal curve, and this type of structural support provided by the spine can result in some degree of deformity [2]. Spinal deformities can be divided into kyphosis, lordosis, and scoliosis, and the degree of deformation is often determined by X-ray radiography and Cobb angle measurements in clinical practice [3]. Poor posture refers to postures that increase joint stress [4] and is often the main cause of spinal deformities. According to [PERSON] et al. [5], poor posture is prevalent among healthy people. People with more severe postural abnormalities increase their risk of experiencing muscle pain. In addition, people suffering from chronic pain exhibit poor control over maintaining an upright posture while sitting and tend to unconsciously change their sitting postures to poor postures over time [6], resulting in a vicious cycle of pain and poor posture. Several devices, including optoelectronic motion analysis systems [7; 8], radiography [9; 10], and pressure sensors [11; 12], are used for monitoring and measuring posture in research and clinical practice. However, the aforementioned methods require the following: (1) specific sites, such as laboratories; (2) technical personnel for data collection; (3) lengthy periods for data analysis; and (4) high system setup costs [13]. Thus, the use of miniaturized monitoring devices [14; 15; 16; 17] has grown rapidly in recent years due to their convenience. In addition, these devices can quickly analyze posture changes and correct poor postures, achieving long-term posture correction. [PERSON] et al.