# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 1 Introduction  
The evolution of Internet of Things systems and multi-sensor devices contributed to the development of systems for human activity monitoring. One set of applications of these technologies is improving the independent living and rehabilitation of older adults and people with special needs [1]. Likewise, there are approaches for fall detection and risk assessment [2, 3]. Usually, the human activity monitoring systems transmit the collected data to the cloud for real-time processing and further analysis [4]. In light of that, the network conditions become an important factor in facilitating data transfer [5]. Therefore, the development of optimized online systems and test pilots are important. Moreover, these systems should be prepared for older adults, which raises other sets of challenges related to usability and ergonomics; therefore, the resilience of these is essential [6, 7]. Different types of activities may be detected with the inertial sensors available in the mobile devices, including running, walking, walking upstairs, walking downstairs, and standing [8, 9]. For the detection of human activities, one of the possibilities is the use of artificial intelligence methods combined with the capabilities of the mobile devices for the development of monitoring tools anywhere at any time [10]. Still, the data acquisition may have problems related to low memory, power processing, and batterycapacity [6], causing missing or incorrect samples in the acquired data, and consequent incorrections in the recognition of the activities. Likewise, in the case of wearables, frequently, the devices can be misplaced, causing inaccurate, invalid, or missing data. It is one of the main problems related to the development of intelligent systems for the monitoring of different people. To reduce such faulty data and improve the reliability of these systems, data imputation techniques might be applied. The data imputation methods used in the different systems depend on the timing of missing data, which it can be missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) [11; 12]. Generally, the data imputation relies on tree-based approaches [13; 14], multi-matrices factorization model (MMF) [15], clustering techniques, e.g., K-means [16] and K-Nearest Neighbor (KNN) [17], multiple imputation [18], hot/cold imputation, maximum likelihood, Bayesian estimation, and expectation maximization [13; 14; 15; 16]. In this study, we considered the case of not using imputation as a competitor for the data imputation with KNN, as it was commonly used in other data imputation problems in different industries [19; 20]. The motivation of this paper is to improve the results on the human activity recognition (considering five activities, i.e., walking, running, standing, walking upstairs, and walking downstairs) by integrating the data imputation algorithm in the data processing pipeline. After this, the whole pipeline consists of data acquisition, data cleaning, identification of the number of missing samples, data segmentation, data imputation, and data. For the data acquisition and cleaning, the previously implemented techniques were used [8; 9]. After the identification of missing samples and data segmentation, the data imputation was implemented with the KNN imputation algorithm [17] for the estimation of the values of the different datasets to fulfill the number of outputs correctly. The values of each axis separately in the raw sensory measurements were imputed separately. The proposed method in this paper uses three inertial sensors, i.e., accelerometer, magnetometer, and gyroscope, with the same frequency of acquisition.