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"The significant motion sensor is a new sensor that promises motion detection at low power consumption. Despite that promise, no known research has explored the usage of this sensor, especially in mobile sensing research. In this study, we explore the utilization of this significant motion sensor for continuous motion and location sampling in a mobile sensing application. A location sensor is known for its expensive power consumption in retrieving the location data, and continuously sampling from it will quickly deplete a smartphone battery. We experiment with two sampling strategies that utilize this significant motion sensor to achieve low power consumption during continuous sampling. One strategy involves utilizing the sensor naively, while the other involves combining with the duty cycle. Both strategies achieve low energy consumption, but the one that combines with the duty cycle achieves lower energy consumption. By utilizing this sensor, mobile sensing research especially that samples data from location or motion sensors, will be able to achieve lower energy consumption."

"ABSTRAKHuman Activity Recognition merupakan sebuah teknologi yang penting karena dapat diimplementasikan dalam berbagai kebutuhan manusia sehari-hari, seperti mengenai kesehatan manusia. Tujuan dari Human Activity Recognition adalah untuk mengidentifikasi aktivitas manusia yang umum, dimana data yang diterima dapat diteliti lebih lanjut. Seiring perkembangan teknologi, keberadaan komputer dan smartphone sudah tidak dapat dipisahkan lagi dalam kehidupan dan aktivitas manusia. Perkembangan teknologi ini membuat sebuah smartphone dapat memiliki berbagai jenis sensor. Sensor-sensor yang terdapat pada smartphone dapat digunakan untuk melakukan Human Activity Recognition dengan mudah. Contoh sensor pada smartphone yang dapat digunakan untuk melakukan Human Activity Recognition adalah sensor accelerometer untuk mengukur perpindahan. Penelitian ini membuat sebuah aplikasi berbasis Android untuk membaca input dari sensor, diolah dengan library neural network Long Short-Term Memory, lalu menghasilkan output yang sesuai. Hasil output yang dimaksud adalah kondisi dari aktivitas manusia yang diteliti, yaitu kondisi berdiri, berjalan, berlari, duduk, menaiki tangga, dan menuruni tangga.

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ABSTRACTHuman Activity Recognition is an important technology because it can be implemented to many human problems, such as healthcare. The main purpose for Human Activity Recognition is to recognize common, simple human activities, where the data received can be researched further. With the development of technology these days, the presence of computer and smartphone cant be removed from daily human activities. This technology development made a smartphone that has been integrated with all kind of sensors. An example of sensor that can be used to do a Human Activity Recognition are accelerometer to measure movement. This research made an Android-based application that will read input from these sensors, processed by neural network Long Short-Term Memor y library, and finally produced the intended output. The outputs are the current activity of user thats been researched on, such as standing, walking, running, sitting, walking upstair, or walking downstair."