I-Corps: Fall prediction and prevention by an artificial compound eye camera system

这个i-Corps项目的更广泛的影响/商业潜力是预测和避免因各种与年龄相关的慢性疾病(如糖尿病、阿尔茨海默病和其他痴呆症、帕金森氏病和心力衰竭)而导致跌倒风险较高的老年人跌倒和与跌倒相关的伤害/死亡。在65岁及以上的老年人中，跌倒是造成意外伤害的主要原因，是导致死亡的第七大原因，仅在美国，每年就有大约300万人去急诊科就诊。预防跌倒不仅可以改善整体健康状况，挽救生命，还可以通过减少医疗和急诊科就诊费用来造福社会。具体地说，拟议技术的成功商业化可以为所有高级护理利益相关者提供价值，如医院、包括长期护理机构和家庭成员在内的护理人员、政府和私人保险公司以及与年龄相关的学术机构和辅助设备制造商。这个i-Corps项目将进一步开发一种人工复眼相机(ACECam)系统，可以从跌倒风险较高的老年人的日常生活中提取与临床相关的步态和活动数据，如步态速度和坐到站的时间。通过将创新的复眼光学与现成的图像传感器相集成，ACECam技术可以通过基于卷积神经网络开发的临床相关步态和运动数据的算法，显著降低运动跟踪和动作识别的计算成本。此外，ACECam对于老年人的日常生活并不引人注目，因为它不需要目前步态/运动跟踪可穿戴设备中常见的可穿戴传感器系统。ACECam获得的原始数据包含有限的视觉信息，因此将保护用户的视觉隐私。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is to predict and avert falls and fall-related injuries/deaths of older adults with high risks of fall due to various age-related chronic diseases such as diabetes, Alzheimer's disease, and other dementias, Parkinson's disease and heart failure. Falls are a leading cause of unintentional injuries and seventh leading cause of mortality among older people aged 65 and over, which leads to approximately 3 million emergency department visits per year in U.S. alone. Prevention of falls will not only improve the overall health outcomes and save lives, but also will benefit society by reducing the expenses in medical treatment and emergency department visits. Specifically, the successful commercialization of the proposed technology could provide value to all senior care stakeholders such as hospitals, caregivers including long-term care facilities and family members, government and private insurers and age-related academic institutions and assisted device manufacturers.This I-Corps project will further develop an artificial compound eye camera (ACECam) system that can extract clinically-relevant gait and mobility data, such as gait speed and sit-to-stand time, from the daily life of older adults who can have higher risk of falls. By integration of innovative compound eye optics with off-the-shelf image sensors, the ACECam technology can significantly reduce the computational costs of motion tracking and action recognition with the algorithms for clinical-relevant gait and mobility data developed based on convolutional neural network. Additionally, ACECam is unobtrusive for the daily life of older adults by not requiring wearable sensor systems that are commonly applied in current gait/motion tracking wearables. The raw data acquired by ACECam contains limited visual information and so will protect visual privacy of the users.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.