STRETCH: Socio-Technical Resilience for Enhancing Targeted Community Healthcare

STRETCH：增强有针对性的社区医疗保健的社会技术弹性

• 批准号: EP/P01013X/1
• 负责人: Blaine Price
• 金额: $ 133.73万
• 依托单位: The Open University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP01013X%2F1
• 关键词: STRETCH; Socio; Technical; Resilience; Enhancing

Treating older adults for medical conditions is complicated because they may need treatment for multiple conditions and they may also have chronic conditions such as reduced strength, mobility, hearing, eyesight or cognitive impairments such as dementia. This means hospital in-patient treatments may take longer and they may be unable to leave hospital to recover at home if they do not have a spouse/partner or family/friends/neighbours able to look after them. Older adults recovering at home frequently rely on 'circles of support' which range from relatives and neighbours, to the voluntary sector, social workers, paid carers, and medical professionals. The STRETCH project aims to help coordinate these circles of support with both wearable and smart home technologies to enhance the social and technical resilience of these circles of support. This should have the double benefit of increasing NHS capacity to cope with increasing numbers of older patients while improving care by making sure that medical professionals have timely and accurate information at all times about their patients. By having intelligently combined both the human and sensor-based sources of data, physicians will be able to recognize when a patient is deteriorating and intervene early to pre-empt problems or longer hospital stays.

治疗老年人的疾病是复杂的，因为他们可能需要治疗多种疾病，他们也可能有慢性疾病，如力量、行动能力、听力、视力或认知障碍，如痴呆症。这意味着住院治疗可能需要更长的时间，如果他们没有配偶/伴侣或家人/朋友/邻居能够照顾他们，他们可能无法出院在家康复。在家中康复的老年人经常依靠从亲戚和邻居到志愿部门、社会工作者、付费照顾者和医疗专业人员的“支持圈”。Stretch项目旨在帮助协调这些支持圈与可穿戴和智能家居技术，以增强这些支持圈的社会和技术弹性。这应该有双重好处，即增加NHS的能力，以应对越来越多的老年患者，同时通过确保医疗专业人员随时获得关于他们患者的及时和准确的信息来改善护理。通过智能地结合人类和基于传感器的数据源，医生将能够识别患者的病情恶化并及早干预，以预防问题或更长的住院时间。

项目成果

Significant features for human activity recognition using tri-axial accelerometers

使用三轴加速度计进行人体活动识别的重要特征

• DOI: 10.34961/researchrepository-ul.24197772.v1
• 发表时间: 2023
• 作者: Bennasar M

Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers.

• DOI: 10.3390/s22197482
• 发表时间: 2022-10-02
• 期刊: Sensors (Basel, Switzerland)
• 作者: Bennasar M;Price BA;Gooch D;Bandara AK;Nuseibeh B
• 通讯作者: Nuseibeh B

Socio-Technical Resilience for Community Healthcare

社区医疗保健的社会技术弹性

• DOI: 10.1145/3597512.3599720
• 发表时间: 2023
• 作者: Bennaceur A

Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques.

用最少的患者临床变量准确预测负荷超声心动图结果：使用机器学习技术的验证研究。

• DOI: 10.2196/16975
• 发表时间: 2020
• 期刊: JMIR cardio
• 作者: Bennasar M

Knowledge-Based Architecture for Recognising Activities of Older People

• DOI: 10.1016/j.procs.2019.09.214
• 发表时间: 2019
• 作者: Mohamed Bennasar;B. Price;Avelie Stuart;D. Gooch;Ciaran Mccormick;Vikram Mehta;L. Clare;A. Bennaceur;Jessica Cohen;A. Bandara;M. Levine;B. Nuseibeh