The value of play has mainly stayed consistent throughout time. Playing is, without a doubt, one of the essential things we can do. Playing in addition to supporting motor, neurological, and social development improves adaptation by encouraging people to explore diverse perspectives on the world and assisting them in developing methods for dealing with problems in a safe setting. The way we play and what we play with have been heavily affected by the quickly evolving technology shaping our daily lives. Artificial intelligence (A.I.) is now found in many products, including vehicles, phones, and vacuum cleaners. This extends to children's items, with the creation of an "Internet of Toys." Many learning, remote control, and app-integrated toys include innovative playthings that employ speech recognition and machine learning to communicate with users. This study examines the impact of technology adoption on the success and failure of two toys industry – Hasbro, Inc and Toys R Us, Inc. The research methodology of this study is based on case studies where the comparison of the two industries was made from a few areas. The finding of the study determines that corporations that evolved consistently with the change of technology will continue to grow in the market. In contrast, the corporation that failed to adopt digital transformation will be a force out of the market.
Gait has been used in many research area including medical and health. One of the ways to capture gait signal is by using the accelerometer sensor in the smartphone. In this work, gait signal is used to identify a person. The accuracy of the gait recognition while the phone held in the palm is evaluated. Besides that, the factor of linear interpolation is examined. Lastly, k-NN, MLP and SVM algorithm are compared in determining the best accuracy that works best with the OvO classifier model. From the experiment, it can be seen that the gained accuracy for k-NN and MLP are both 96.7% with only 1 misclassified. Although the work is not related to medical and health, somehow it could provide the basis in healthcare related application. From the result, it is possible in adopting the proposed method in classifying decision based on the gait signal for medical and health purposes.
Gait signal of a person can be easily obtained using a smartphone sensor. To get the source of the signal, the smartphone need to be placed in the pocket, pouch or attached to other parts of the body. In the real world application, it is hard to place the device on the mentioned position. The easiest way is to put it on hand. In another issue, the single magnitude is known in the use of multiple orientations. However, this method may discard useful features for machine learning classification. Another problem is that the signal captured using a smartphone is not in a fix sampling rate and in the small distance, hence interpolation needs to be applied so that the sampling can be in a fix sequence with more fix point data. However, too much application of interpolation may result in low prediction rate. Finally, a multiclass dataset may contain overlapped class boundary which produces low accuracy on a single classifier mapping. In this paper, hand based smartphone placement position is implemented and evaluated. Single magnitude application is also evaluated in representing multiple positions of a person into one signal. Besides that, the linear interpolation factor is introduced in sampling the signal. Lastly, OvO classification model is implemented in binarizing the multiclass gait dataset. From the experiment, it shows that using the mentioned method do produce satisfactory result hence opening a new gateway in a better gait identification/recognition system.
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