A population group that is often overlooked in the recent revolution of self-tracking is the group of older people. This growing proportion of the general population is often faced with increasing health issues and discomfort. In order to come up with lifestyle advice towards the elderly, we need the ability to quantify their lifestyle, before and after an intervention. This research focuses on the task of activity recognition (AR) from accelerometer data. With that aim, we collect a substantial labelled dataset of older individuals wearing multiple devices simultaneously and performing a strict protocol of 16 activities (the GOTOV dataset, $$N=28$$ N = 28 ). Using this dataset, we trained Random Forest AR models, under varying sensor set-ups and levels of activity description granularity. The model that combines ankle and wrist accelerometers (GENEActiv) produced the best results (accuracy $$>80\%$$ > 80 % ) for 16-class classification. At the same time, when additional physiological information is used, the accuracy increased ( $$>85\%$$ > 85 % ). To further investigate the role of granularity in our predictions, we developed the LARA algorithm, which uses a hierarchical ontology that captures prior biological knowledge to increase or decrease the level of activity granularity (merge classes). As a result, a 12-class model in which the different paces of walking were merged showed a performance above $$93\%$$ 93 % . Testing this 12-class model in labelled free-living pilot data, the mean balanced accuracy appeared to be reasonably high, while using the LARA algorithm, we show that a 7-class model (lying down, sitting, standing, household, walking, cycling, jumping) was optimal for accuracy and granularity. Finally, we demonstrate the use of the latter model in unlabelled free-living data from a larger lifestyle intervention study. In this paper, we make the validation data as well as the derived prediction models available to the community.

中文翻译：

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使用可穿戴传感器进行活动识别以跟踪老年人

在最近的自我追踪革命中，一个经常被忽视的人群是老年人群。越来越多的普通人群经常面临越来越多的健康问题和不适。为了向老年人提出生活方式建议，我们需要能够在干预前后量化他们的生活方式。这项研究的重点是从加速度计数据中进行活动识别 (AR) 的任务。出于这个目的，我们收集了大量同时佩戴多个设备并执行 16 项活动的严格协议的老年人的大量标记数据集（GOTOV 数据集，$$N=28$$N = 28）。使用此数据集，我们在不同的传感器设置和活动描述粒度级别下训练了随机森林 AR 模型。结合脚踝和手腕加速度计（GENEActiv）的模型对 16 级分类产生了最佳结果（准确度 $$>80\%$$ > 80%）。同时，当使用额外的生理信息时，准确度增加（ $$>85\%$$ > 85 % ）。为了进一步研究粒度在我们的预测中的作用，我们开发了 LARA 算法，该算法使用层次本体来捕获先验生物知识来增加或减少活动粒度（合并类）的级别。结果，合并了不同步行速度的 12 级模型表现出高于 $93\%$$ 93 % 的性能。在标记的自由生活飞行员数据中测试这个 12 级模型，平均平衡精度似乎相当高，同时使用 LARA 算法，我们表明 7 级模型（躺下、坐着、站立、家庭、步行、骑自行车、跳跃）对于准确性和粒度是最佳的。最后，我们展示了后一种模型在来自大型生活方式干预研究的未标记自由生活数据中的使用。在本文中，我们将验证数据以及派生的预测模型提供给社区。