CASAS Smart Home dataset - scripted complex activities, activity scores, and cognitive diagnosis

We hypothesize cognitive impairment can be evident in everyday task performance. We also postulate that differences in task performance can be automatically detected between cognitively healthy individuals and those with dementia and mild cognitive impairment (MCI) using smart home and ubiquitous computing technologies. This dataset contains ambient sensor readings collected in the CASAS smart apartment testbed at Washington State University for 179 participants with corresponding cognitive diagnoses. Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas. Data are collected continuously from ambient sensors while participants perform 24 scripted activities. Each sensor reading is reported on a separate line and is described by fields date, time, sensor, and message. The first 8 activities are performed without cues (task step reminders), the second 8 activities are performed with cues when needed, and the last 8 activities, part of a complex day out task, are interwoven in a natural manner without cues. The task list is included in the file activitylist.txt. The file activityscores.txt provides numeric values for activities 1-8 that are based on experimenter assessment of task quality. The file also gives scores for the day out task. Finally, the file diagnosis.txt lists the diagnosis for each participant, coded as the following: 1 = dementia2 = MCI3 = middle age 45-594 = young-old 60-745 = old-old 75+6 = other medical7 = watch/at risk - follow longitudinally8 = younger adult9 = younger adult, English second language10 = diagnosis not available The sensors are categorized (and named) as: M01 - M51: PIR motion detectors (ON when detected motion starts and OFF when it stops) I01 - I10: item use sensors (PRESENT or ABSENT indicating item is on sensor or not) D01 - D019: door sensor on cabinets and doors (OPEN or CLOSE) P001 and P002: current eletricity consumption T001 - T006: ambient temperature sensors BATP and BATV: sensor battery levels The floorplan and sensor layout are provided in the file Chinook.jpg.我们提出假说：认知障碍可在日常任务执行过程中显现。同时我们假定，借助智能家居与普适计算技术，可自动检测认知健康个体与痴呆、轻度认知障碍（MCI）患者之间的任务表现差异。本数据集包含华盛顿州立大学CASAS智能公寓测试平台中采集的环境传感器读数，涉及179名已获取认知诊断结果的参与者。 注：其他CASAS智能家居与智能手表数据集亦可获取，详情请访问https://zenodo.org/communities/casas。 数据由环境传感器持续采集，参与者在此期间完成24项脚本化任务。每条传感器读数单独占一行，由日期、时间、传感器编号及报文四个字段构成。前8项任务无提示（无任务步骤提醒），中间8项任务在需要时将提供提示，最后8项任务属于复杂的外出一日行程任务，将以自然方式交织进行且不提供提示。任务清单已包含在activitylist.txt文件中。 文件activityscores.txt提供了第1至8项任务的数值评分，评分基于实验人员对任务完成质量的评估。该文件同时提供了外出一日行程任务的评分。最后，文件diagnosis.txt列出了每名参与者的诊断结果，编码规则如下： 1 = 痴呆 2 = MCI（轻度认知障碍） 3 = 中年人群（45-59岁） 4 = 低龄老年人（60-74岁） 5 = 高龄老年人（75岁及以上） 6 = 其他内科疾病 7 = 佩戴设备/存在风险——需长期纵向随访 8 = 年轻成年人 9 = 年轻成年人，英语为第二语言 10 = 诊断结果不可用 传感器分类（及命名规则）如下： M01 - M51：被动红外（PIR）运动探测器（检测到运动时输出ON，运动停止时输出OFF） I01 - I10：物品使用传感器（输出PRESENT或ABSENT，以标识物品是否放置于传感器上） D01 - D019：柜门与房门的门磁传感器（输出OPEN或CLOSE） P001与P002：电流能耗传感器 T001 - T006：环境温度传感器 BATP与BATV：传感器电池电量 文件Chinook.jpg中提供了房屋平面图与传感器布局图。