Self-tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data

Abstract

Abstract. Currently, there is a trend to promote personalized health care in order to prevent diseases or to have a healthier life. Using current devices such as smart-phones and smart-watches, an individual can easily record detailed data from her daily life. Yet, this data has been mainly used for self-tracking in order to enable personalized health care. In this paper, we provide ideas on how process mining can be used as a ne-grained evolution of traditional self-tracking. We have applied the ideas of the paper on recorded data from a set of individuals, and present conclusions and challenges.

Towards IoT-driven Process Event Log Generation for Conformance Checking in Smart Factories

“…First of all, event abstraction is needed to map multiple fine-grained sensor data to coarse-grained process events [18], e. g., with the help of CEP [6], which also supports data pre-processing and context enrichment [2]; or with machine learning approaches such as clustering [19] or supervised learning [20]. Wanner et al combine these two methods based on expert knowledge and observations in the context of a smart factory [21].…”

“…The Internet of Things (IoT) enables the digitization of the physical world through interconnected devices, thereby providing access to vast amounts of data that can be used to develop digital services in several application domains including Business Process Management (BPM) [1]. The data generated by sensing devices and smart objects allows for the continuous monitoring of the IoT devices and their surroundings, providing new opportunities for analysis and optimization of the processes performed in IoT environments, e. g., through process mining approaches [2], [3].…”

Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

“…Cited research works location event log preparation and interpretation (Senderovich et al, 2016;Muzammal et al, 2018;Wan et al, 2017;Namaki Araghi et al, 2018a) Use of location data for extraction of knowledge (Hwang and Jang, 2017;Sztyler et al, 2016;Ertek et al, 2017;Mazimpaka and Timpf, 2016), (Rojas et al, 2017a;Zheng, 2015a;Tanuja and Govindarajulu, 2017;Ramos et al, 2017;Garaeva et al, 2017;Bao and Wang, 2017), (Zhenjiang et al, 2017;Aryal and Sujing Wang, 2017;Tanuja and Govindarajulu, 2016;Feng Ling et al, 2016), (Lamr and Skrbek, 2016;Blank et al, 2016;Fernandez-Llatas et al, 2015;Zheng, 2015b), (Liao et al, 2015;Tang et al, 2015;Miclo et al, 2015;Jin et al, 2015;Martinez-Millana et al, 2019), (Dogan et al, 2019;Namaki Araghi et al, 2019;Namaki Araghi et al, 2018a;Namaki Araghi et al, 2018b;Araghi et al, 2018) Figure 3: Analysis of the literature of process mining, indoor localization systems, and data mining relevant to the approaches in which the location data were used.…”

Applications of non‐invasive sensor devices to personalised health care

“…A spatio–temporal routine mining model (STRMM) is established to describe the daily commuting behaviour of mobile users and to predict the trajectory in [22]. The above two papers judge human activities from the perspective of analysing data, while the study in [23] shows how to combine process mining with self‐tracking. Inspired by the method mentioned in [23], we collect activity information of seven people through the GPS system of the mobile phone and employ our production to collect heart rate information.…”