I-Corps: Lymphedema Intervention Exercise for Breast Cancer Survivors

这个i-Corps项目的更广泛的影响/商业潜力是，它使数百万有慢性淋巴浮肿风险或已经患上慢性淋巴浮肿的人受益，包括接受过乳腺癌和其他癌症(头颈部、前列腺癌、黑色素瘤、妇科癌症)治疗的患者。淋巴水肿是一种进行性和慢性肿胀，在癌症治疗后有许多痛苦的症状。每年，全球有138万女性和美国超过23万女性被诊断出患有乳腺癌。至少有20%-40%的乳腺癌幸存者患上了淋巴水肿，其余的人都面临着终身淋巴水肿的风险。该项目进一步开发了一种淋巴疗法，有效地降低了乳腺癌幸存者的淋巴浮肿风险，缓解了淋巴浮肿症状。这种方法可能会更广泛地应用于患有慢性心力衰竭、终末期肾脏疾病和高血压并有类似异常液体堆积症状的个人。这个I-Corps项目探索了淋巴疗法的商业潜力。核心技术包括一个物理治疗系统，该系统可以自动、准确地跟踪患者的运动，并根据所需的运动进行评估。该系统提供关于患者是否正确进行锻炼以及患者应该如何调整他们的运动的即时反馈。此外，该系统将被设计为激励患者正确和定期地进行治疗。该系统将利用正在进行的研究项目中开发的新型计算机视觉和机器学习算法，以克服传感器在检测关节位置方面的限制以及所需运动的正常范围的显著可变性，这些都是当前基于传感器的理疗系统面临的主要技术障碍。

The broader impact/commercial potential of this I-Corps project is its benefit for millions of individuals who are at risk for or have developed chronic lymphedema, including patients who have been treated for breast cancer and other cancers (head and neck, prostate, melanoma, gynaecological cancer). Lymphedema is a progressive and chronic swelling with many distressful symptoms after cancer treatment. Each year, 1.38 million women worldwide and more than 230,000 women in the US are diagnosed with breast cancer. At least 20-40% of breast cancer survivors have developed lymphedema and the rest of them are at lifelong risk for lymphedema. This project further develops a lymphatic therapy effective in reducing the risk of lymphedema and relieving lymphedema symptoms for breast cancer survivors. The approach may potentially be applied more broadly to individuals with chronic heart failure, end-stage renal disease and hypertension with similar abnormal fluid accumulation symptoms.This I-Corps project explores the commercial potential of a lymphatic therapy. The core technology consists of a physical therapy system that can automatically and accurately track a patient's movement and evaluate it against the desired movement. The system provides instant feedback on whether a patient is performing an exercise correctly and how should the patient make adjustments in their movement. Furthermore, the system will be designed to motivate the patient to perform the therapy correctly and regularly. The system will leverage the novel computer vision and machine learning algorithms being developed in an on-going research project in order to overcome the sensor constraints in detecting joint positions and the significant variability of the normal range of the desired movement, which are the major technical obstacles faced by current sensor-based physical therapy systems.