Abstract
Autonomous devices able to evaluate diverse situations without external help have become especially relevant in recent years because they can be used as an important source of relevant information about the activities performed by people (daily habits, sports performance, and health-related activities). Specifically, the use of this kind of device in childhood games might help in the early detection of developmental problems in children. In this paper, we propose a method for the detection and classification of movements performed with an object, based on an acceleration signal. This method can automatically generate patterns associated with a given movement using a set of reference signals, analyze sequences of acceleration trends, and classify the sequences according to the previously established patterns. This method has been implemented, and a series of experiments has been carried out using the data from a sensor-embedded toy. For the validation of the obtained results, we have, in parallel, developed two other classification systems based on popular techniques, i.e., a similarity search based on Euclidean distances and machine-learning techniques, specifically a support vector machine model. When comparing the results of each method, we show that our proposed method achieves a higher number of successes and higher accuracy in the detection and classification of isolated movement signals as well as in sequences of movements.
Highlights
Playing is one of the most important activities during human development, especially at early ages
The proposed optimization system in section III-B obtains the tuple of optimum variables and the pattern for the further detection of a type of movement
The utility of the optimization system is shown in 1, illustrating that the variables used for analyzing a signal depend on the type of movement searched
Summary
Playing is one of the most important activities during human development, especially at early ages. It is a key activity for the child learning process and an indicator of psycho-motor development across the different growth phases. Child development experts (psychologists, physiotherapists, educators, etc.) have developed different tools and scales to evaluate and monitor childhood development that extensively use toys and playing activities [1], [2]. The idea of designing smart toys able to provide useful data for development experts is a very promising line of research [11], in which some psychology scale-based smart toys and an IoT platform were designed to provide this type of information. There is a description of some of the devices designed for the project [12]: A set of ‘‘Smart Cubes’’ to perform activities such as building a tower or other structures
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