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.
Highlights
The spine is an important bony structure that provides support to the human body while providing other main functions, such as protecting the spinal cord and nerve roots, resisting external forces, and enabling the performance of all human body movements
All collected data were used to train the same machine learning model, and performance was evaluated by comparing F1 scores (Equation (1)) between different sensor combinations, where the F1 score is the harmonic mean of precision (TP/(TP + FP) and recall (TP/(TP + FN), TP is the number of true positives, FP is the number of false posi4tiovfe1s3, and FN is the number of false negatives [32]
All collected data were used to train the same machine learning model, and performance was evaluated by comparing F1 scores (Equation (1)) between different sensor combinations, where the F1 score is the harmonic mean of precision (TP/(TP + FP) and recall (TP/(TP + FN), TP is the number of true positives, FP is the number of false positives, and FN is the number of false negatives [32]
Summary
The spine is an important bony structure that provides support to the human body while providing other main functions, such as protecting the spinal cord and nerve roots, resisting external forces, and enabling the performance of all human body movements. Its kyphotic curve allows the spine to bear loads 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. Poor posture can cause acute or chronic forms of spinal deformities. 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]. 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
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