Deep learning-based companion robot on senile dementia patients

医学 痴呆 统计显著性 小型精神状态检查 阿尔茨海默病 精神状态 疾病 精神科 内科学
作者
Tao Jin,Songzhe Fu,Danping Wu,Jiang Yong-zeng,Yiping Wang
出处
期刊:CNS spectrums [Cambridge University Press]
卷期号:28 (S2): S3-S3
标识
DOI:10.1017/s1092852923002547
摘要

Background There are currently at least 50 million dementia patients worldwide, and this number is expected to reach 152 million by 2050, of which about 60-70% will be Alzheimer’s patients. The companion robot based on deep learning is a product of the development of artificial intelligence technology, which is of great significance to the physical and mental health of the elderly, so it is used in the research on the treatment of Alzheimer’s patients. Subjects and Methods 100 patients with Alzheimer’s disease in a hospital were selected for the study, and 50 patients were randomly divided into experimental group and control group. In the experiment, 50 patients with Alzheimer in the experimental group used a companion robot based on deep learning for auxiliary treatment while carrying out daily treatment. The control group of 50 patients did not receive any adjuvant therapy in addition to daily treatment. After three months of treatment, the study used the 3D-CAM and the mini–mental state examination (MMSE) to collect the treatment status of all patients, and used the SPSS23.0 statistical software to statistically analyze the collected data. Results After statistical analysis, the results of the two groups were obtained. The scores of 3D-CAM and MMSE in the experimental group were significantly higher than those in the control group and the difference was statistically significant. Conclusions Companion robots based on deep learning are helpful in the treatment of Alzheimer’s patients. They can improve the therapeutic effect and have certain social value. Acknowledgement The Fundamental Research Funds in Heilongjiang Provincial Universities (No.135309356); Qiqihar University Young Teachers’ Scientific Research Initiation Support Program (No.2012k-M17).

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
liang_zai完成签到 ,获得积分10
1秒前
田国兵完成签到,获得积分10
1秒前
qinghe完成签到 ,获得积分10
1秒前
siri完成签到,获得积分10
1秒前
百合子发布了新的文献求助10
2秒前
2秒前
七七完成签到 ,获得积分10
2秒前
周周完成签到 ,获得积分10
2秒前
霸王龙完成签到,获得积分10
3秒前
贝儿完成签到,获得积分10
3秒前
MSBLANK完成签到,获得积分10
3秒前
量子星尘发布了新的文献求助10
3秒前
Febrine0502完成签到,获得积分10
3秒前
PGH发布了新的文献求助10
4秒前
明亮紫夏完成签到,获得积分10
4秒前
zzz完成签到,获得积分10
4秒前
jlwang发布了新的文献求助10
4秒前
yuan完成签到,获得积分10
5秒前
shadow完成签到,获得积分10
6秒前
evelyn完成签到 ,获得积分10
6秒前
大力蚂蚁完成签到,获得积分10
7秒前
滴滴滴完成签到,获得积分10
7秒前
8秒前
啦啦啦123完成签到,获得积分10
8秒前
9秒前
难受的难受完成签到,获得积分10
9秒前
9秒前
吕小布完成签到,获得积分10
9秒前
脑洞疼应助华清引采纳,获得10
9秒前
枣树先生完成签到 ,获得积分10
9秒前
11秒前
田様应助卿卿采纳,获得10
11秒前
12秒前
12秒前
leinuo077完成签到,获得积分10
12秒前
秦艽完成签到,获得积分10
12秒前
天才c完成签到,获得积分10
13秒前
漂亮的访冬完成签到,获得积分10
13秒前
量子星尘发布了新的文献求助10
13秒前
Tianji关注了科研通微信公众号
14秒前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2700
Neuromuscular and Electrodiagnostic Medicine Board Review 1000
Statistical Methods for the Social Sciences, Global Edition, 6th edition 600
こんなに痛いのにどうして「なんでもない」と医者にいわれてしまうのでしょうか 510
The First Nuclear Era: The Life and Times of a Technological Fixer 500
ALUMINUM STANDARDS AND DATA 500
Walter Gilbert: Selected Works 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3666902
求助须知:如何正确求助?哪些是违规求助? 3225730
关于积分的说明 9765171
捐赠科研通 2935586
什么是DOI,文献DOI怎么找? 1607790
邀请新用户注册赠送积分活动 759374
科研通“疑难数据库(出版商)”最低求助积分说明 735302