The majority of older people wish to live independently at home as long as possible despite having a range of age-related conditions including cognitive impairment. To facilitate this, there has been an extensive focus on exploring the capability of new technologies with limited success. This paper investigates whether MS Kinect (a motion-based sensing 3-D scanner device) within the MiiHome (My Intelligent Home) project in conjunction with other sensory data, machine learning and big data techniques can assist in the diagnosis and prognosis of cognitive impairment and hence prolong independent living. A pool of Kinect devices and various sensors powered by minicomputers providing internet connectivity are being installed in up to 200 homes. This enables continuous remote monitoring of elderly residents living alone. Passive and off-the-shelf sensor technologies were chosen to implement data acquisition specifically from sources that are part of the fabric of the homes, so that no extra effort is required from the participants. Various constraints including environmental, geometrical and big data were identified and appropriately dealt with. A visualization tool (MAGID) was developed for validation and verification of numerous behavioural activities. Then, a subset of data, from twelve pensioners aged over 65 with age-related cognitive decline and frailty, were collected over a period of 6 months. These data were subjected to several machine learning algorithms (multilayer perceptron neural network, neuro-fuzzy and deep learning) for classification and to extract routine behavioural patterns. These patterns were then analysed further to ascertain any health-related information and their attributes. For the first time, important routine behaviour related to Activities of Daily Living (ADL) of elderly people with cognitive and physical decline has been learnt by machine learning techniques from selected sample data obtained by MS Kinect. Medically important behaviour, e.g. eating, walking, sitting, was best learnt by deep learning with accuracy of 99.30% during training stage and average error rate of 1.83% with maximum of 12.98% during the implementation phase. Observations obtained from the application of the above learnt behaviours are presented as trends over a period of time. These trends, supplemented by other sensory signals, have provided a clearer picture of physical (in)activities (including falls) of the pensioners. The calculated behavioural attributes related to key indicators of health events can be used to model the trajectory of health status related to cognitive decline in a home setting. These results, based on a small number of elderly residents over a short period of time, imply that within the results obtained from the MiiHome project, it is possible to find indicators of cognitive decline. However, further studies are needed for full clinical validation of these indications in conjunction with assessment of cognitive decline of the participants.

尽管有一系列与年龄有关的条件，包括认知障碍，但大多数老年人还是希望尽可能长的独立生活。为了促进这一点，已经将广泛的精力集中在探索新技术的能力上，但收效甚微。本文研究了MiiHome（我的智能家居）项目中的MS Kinect（基于运动的感测3D扫描仪设备）是否与其他感官数据，机器学习和大数据技术相结合，是否可以帮助认知障碍的诊断和预后因此延长了独立生活。由多达200个家庭安装的Kinect设备池和由微型计算机提供互联网连接的各种传感器正在安装中。这样可以连续远程监视独居的老年居民。选择了无源和现成的传感器技术来专门从房屋结构的一部分中进行数据采集，因此参与者无需付出额外的努力。确定并适当处理了各种约束，包括环境，几何和大数据。开发了可视化工具（MAGID）来验证和验证许多行为活动。然后，在六个月的时间内收集了来自十二名年龄与年龄相关的认知能力下降和虚弱的65岁以上养老金领取者的数据子集。这些数据经过几种机器学习算法（多层感知器神经网络，神经模糊和深度学习）进行分类，并提取常规行为模式。然后对这些模式进行进一步分析，以确定与健康有关的任何信息及其属性。机器学习技术首次从MS Kinect获得的选定样本数据中学习了与认知和身体发育下降的老年人的日常生活活动（ADL）有关的重要常规行为。深度学习最好地学习医学上重要的行为，例如进食，散步，坐下，在训练阶段的准确性为99.30％，在实施阶段的平均错误率为1.83％，最大值为12.98％。从上述学到的行为的应用获得的观察结果表示为一段时间内的趋势。这些趋势以及其他感官信号的补充，使养老金领取者的身体（活动）活动（包括跌倒）更加清晰。与健康事件的关键指标有关的计算出的行为属性可用于对与家庭环境中的认知下降有关的健康状况的轨迹进行建模。这些结果是在短时间内基于少数老年人的结果得出的，表明从MiiHome项目获得的结果中，有可能找到认知能力下降的指标。但是，还需要进一步的研究，以对这些指征进行全面的临床验证，并评估参与者的认知能力下降。暗示在从MiiHome项目获得的结果中，有可能找到认知能力下降的指标。但是，还需要进一步的研究，以对这些指征进行全面的临床验证，并评估参与者的认知能力下降。暗示在从MiiHome项目获得的结果中，有可能找到认知能力下降的指标。但是，还需要进一步的研究，以对这些指征进行全面的临床验证，并评估参与者的认知能力下降。