Predictive Biomechanics for Modelling Gait Stability and Falls Prediction

高效、适应性的运动对我们的独立性至关重要，但它会受到创伤、衰老和其他损伤引起的神经肌肉疾病的不利影响，这些损伤会增加平衡丧失和跌倒的风险。该项目研究了从传统的基于实验室的、回顾性的、步态研究范式，到使用预测性生物力学进行现实世界步态监测的非凡可能性。通过使用人工智能，可穿戴传感器的数据将预测平衡丢失并提醒用户。研究结果将为开发可穿戴系统提供基础知识，以减少跌倒的灾难性影响，节省公共卫生成本，提高行动不便者的生活质量。

Efficient, adaptive locomotion is critical to our independence, but it is adversely affected by neuromuscular disorders due to trauma, ageing and other impairments that increase the risk of balance loss and falling. This project investigates the extraordinary possibilities of advancing from the traditional laboratory-based, retrospective, gait research paradigm, to real-world gait monitoring using predictive biomechanics. By employing artificial intelligence, wearable sensors' data will predict balance loss and alert the user. The outcome will be fundamental knowledge for developing wearable systems to reduce the catastrophic impact of falls, with public health cost savings and improved quality of life for people with restricted mobility.