Forward sagittal alignment affects physical performance, is associated with pain and impacts the health-related quality of life of the elderly. Interventions that help seniors to improve sagittal balance are needed to inhibit the progression of pain and disability. A motion-sensing video game (active game) is developed in this study to monitor sitting and standing postures in real-time and facilitate the postural learning process by using optical sensors to measure body movement and a video game to provide visual feedback. Ten female subjects (mean age: 60.0 ± 5.2 years old; mean BMI: 21.4 ± 1.9) with adult degenerative scoliosis (mean major Cobb’s angle: 38.1°± 22.7°) participate in a 6-week postural training programme with three one-hour postural training sessions a week. Eleven body alignment measurements of their perceived “ideal” sitting and standing postures are obtained before and after each training session to evaluate the effectiveness of postural learning with the game. The participants learn to sit and stand with increased sagittal alignment with a raised chest and more retracted head position. The forward shift of their head and upper body is significantly reduced after each training session. Although this immediate effect only partially sustained after the 6-week program, the participants learned to adjust their shoulder and pelvis level for a better lateral alignment in standing. The proposed postural training system, which is presented as a gameplay with real-time visual feedback, can effectively help players to improve their postures. This pilot feasibility study explores the development and initial assessment of a motion-based video game designed for postural training in older adults with adult degenerative scoliosis, and demonstrates the usability and benefits of active gameplay in motor training.

The spine is an important part of the human body. Thus, its curvature and shape are closely monitored, and treatment is required if abnormalities are detected. However, the current method of spinal examination mostly relies on two-dimensional static imaging, which does not provide real-time information on dynamic spinal behaviour. Therefore, this study explored an easier and more efficient method based on machine learning and sensors to determine the curvature of the spine. Fifteen participants were recruited and performed tests to generate data for training a neural network. This estimated the spinal curvature from the readings of three inertial measurement units and had an average absolute error of 0.261161 cm.