In response to the social issue of the increasing number of elderly
vulnerable groups going missing due to the aggravating aging population in
China, our team has developed a wearable anti-loss device and intelligent early
warning system for elderly individuals with intermittent dementia using
artificial intelligence and IoT technology. This system comprises an anti-loss
smart helmet, a cloud computing module, and an intelligent early warning
application on the caregiver's mobile device. The smart helmet integrates a
miniature camera module, a GPS module, and a 5G communication module to collect
first-person images and location information of the elderly. Data is
transmitted remotely via 5G, FTP, and TCP protocols. In the cloud computing
module, our team has proposed for the first time a multimodal dangerous state
recognition network based on scene and location information to accurately
assess the risk of elderly individuals going missing. Finally, the application
software interface designed for the caregiver's mobile device implements
multi-level early warnings. The system developed by our team requires no
operation or response from the elderly, achieving fully automatic environmental
perception, risk assessment, and proactive alarming. This overcomes the
limitations of traditional monitoring devices, which require active operation
and response, thus avoiding the issue of the digital divide for the elderly. It
effectively prevents accidental loss and potential dangers for elderly
individuals with dementia.

我们团队开发了一种可穿戴的防丢失设备和智能早期警告系统，用于老年易失藤症患者，利用人工智能和物联网技术。

老年间歇性痴呆患者多模态危险状态识别与早期预警系统

A Multimodal Dangerous State Recognition and Early Warning System for  Elderly with Intermittent Dementia

Memory disorders are a central factor in the decline of functioning and daily
activities in elderly individuals. The confirmation of the illness, initiation
of medication to slow its progression, and the commencement of occupational
therapy aimed at maintaining and rehabilitating cognitive abilities require a
medical diagnosis. The early identification of symptoms of memory disorders,
especially the decline in cognitive abilities, plays a significant role in
ensuring the well-being of populations. Features related to speech production
are known to connect with the speaker's cognitive ability and changes. The lack
of standardized speech tests in clinical settings has led to a growing emphasis
on developing automatic machine learning techniques for analyzing naturally
spoken language. Non-lexical but acoustic properties of spoken language have
proven useful when fast, cost-effective, and scalable solutions are needed for
the rapid diagnosis of a disease. The work presents an approach related to
feature selection, allowing for the automatic selection of the essential
features required for diagnosis from the Geneva minimalistic acoustic parameter
set and relative speech pauses, intended for automatic paralinguistic and
clinical speech analysis. These features are refined into word histogram
features, in which machine learning classifiers are trained to classify control
subjects and dementia patients from the Dementia Bank's Pitt audio database.
The results show that achieving a 75% average classification accuracy with only
twenty-five features with the separate ADReSS 2020 competition test data and
the Leave-One-Subject-Out cross-validation of the entire competition data is
possible. The results rank at the top compared to international research, where
the same dataset and only acoustic features have been used to diagnose
patients.

记忆障碍是老年人功能和日常活动衰退的核心因素。该研究采用自动特征选择和机器学习技术，通过语音分析来诊断认知能力下降和痴呆症患者，取得了高于国际研究水平的分类准确度。