Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.

https://www.mdpi.com/1424-8220/20/9/2702/pdf

Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

This paper proposes a multi-person action recognition system based on frequency modulated continuous wave radar (FMCW). First, a Gaussian mixture model clustering method (GMM) is used to extract the active point cloud data of a single person from the data collected by the radar. Then point cloud nearest neighbor sampling algorithm is proposed for processing the radar point cloud data. Eventually, the long and short-term memory network (LSTM) is used to extract the features between time series data frames to realize action recognition. In this paper, a nearest neighbor sampling algorithm is proposed to solve the problem of sparse point cloud in single-person activity in the radar point cloud data processing. This method selects the reflection point closest to the centroid of the current frame based on the Euclidean distance, and merges multiple frames data with behavior information contained through a sliding window to fill in the information of the current frame and enrich the temporal and spatial sequence data. The radar point cloud data collected is then converted to fixed-dimensional feature information to meet the input requirements of the LSTM network. The experimental results show that the action recognition proposed in this paper has an average recognition accuracy of 98.68% for falling, sitting, and walking in a multi-person activity scene.

FMCW Multi-Person Action Recognition System Based on Point Cloud Nearest Neighbor Sampling Algorithm

Activity recognition is one of the preliminary steps in designing and implementing assistive services in smart homes. Such services help identify abnormality or automate events generated while occupants do, as well as intend to do, their desired activities of daily living (ADLs) inside a smart home environment. However, most of the existing systems are applied for single-resident homes. Multiple people living together create additional complexity in modeling numbers of overlapping and concurrent activities. In this paper, we introduce a hybrid mechanism between ontology-based and unsupervised machine learning strategies in creating activity models used for activity recognition in the context of multi-resident homes. On comparing with related data-driven approaches, the proposed technique is technically and practically scalable to real-world scenarios due to fast training time and easy implementation. An average activity recognition rate of 95.83% on CASAS Spring dataset was achieved and the run time per recognition operation was measured as 12.86 ms.

A Novel Approach of Ontology-Based Activity Segmentation and Recognition Using Pattern Discovery in Multi-resident Homes

Machine learning is a very popular research field, almost every industry is using machine learning to improve the data analysis ability of the industry. In this paper, we mainly analyze the application of machine learning in behavior recognition, meteorology and other industries, and summarize the common methods and development status of machine learning in these two industries.

Review on Machine learning and its application in Atmospheric science and Human Behavior Recognition

Smart home is one of the most important applications of ubiquitous computing. In this work, we propose an infrastructure of Vietnamese Smart homes as well as a training framework for activity recognition and forecast. In this framework, active learning technique is applied and a new mining algorithm is proposed. In addition to activity recognition, a forecast mechanism is also added into the smart home simulation system by using activity sequence as an extra type of in-home contexts. Experiment results show that the system efficiency is improved when compared to the previous work of Enamul Hoque et al.

An approach of using contexts for in-home activity recognition and forecast

Activity clustering and recognition is one of the most important research trends about smart home. Taking place inside a sensor smart home, activities differ from each other at typical characteristics such as sensor sets triggered as well as temporal ones. In this work, we present a smart home infrastructure and propose a method of calculating neighborhood radius for clustering and recognizing in-home activities based on temporal characteristics. Experiment results show that the new method is proved to be easier and more flexible in finding neighborhood radius for clustering than the original DBSCAN algorithm and helps to generate several times as many smart contexts for activity recognition and next-activity forecast as the clustering results reported in Enamul Hoque et al 's research work.

An Algorithmic Method of Calculating Neighborhood Radius for Clustering In-home Activities within Smart Home Environment

A Novel Approach to Clustering Activities Within Sensor Smart Homes

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An Algorithmic Method of Calculating Neighborhood Radius for Clustering In-home Activities within Smart Home Environment

A Novel Approach to Clustering Activities Within Sensor Smart Homes

An approach of using contexts for in-home activity recognition and forecast