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Abstrak :
Sistem pemantauan aktivitas fisik manusia berhasil dibuat dengan menggunakan tiga buah sensor akselerometer 3-sumbu MMA7260Q yang diaplikasikan sebagai pedometer, penentu gerak tubuh, pemantau kecepatan gerak dan jarak tempuh. Dengan menggunakan pengendali mikro ATmega128 data percepatan dikonversi oleh ADC (Analog to Digital Conversion) internal dari pengendali mikro kemudian hasilnya disimpan dalam kartu memori tipe SD yang dilengkapi oleh tampilan waktu dan tanggal pengambilan data dengan menggunakan RTC (Real Time Clock), DS1307 serta mengirim datanya ke PC secara wireless dengan menggunakan Zigbee sebagai wireless adapter. Pemantauan aktivitas dilakukan dengan cara memasang akselerometer di betis, paha dan pinggang pasien. Penentuan gerak tubuh dilakukan dengan melihat keluaran tegangan setiap sensor akselerometer dari masing-masing gerakan. Pengiriman data secara wireless membuat alat ini menjadi portable dengan maksimal jarak antara transmitter dan receiver pada suatu gedung tertutup 20 meter dengan asumsi transmitter berada 1 lantai dibawah receiver dan memiliki 1 sekat penghalang. ......Monitoring system of human physical activities has been successfully constructed using three sensors 3-axis accelerometer MMA7260Q applied as pedometer, decisive gesture, observer velocity, stride, and travelled distance. Using microcontroller Atmel AVR ATmega128 series acceleration data is converted by the internal ADC (Analog to Digital Conversion) and the results are stored in SD card and sending it wirelessly using Zigbee as wireless adapter. Data complemented by the display time and date of data acquisition by using the RTC (Real Time Clock), DS1307. Physical activities monitored by placing accelerometer sensor at the waist, thighs, and calves. Determination of body movements performed by determining the acceleration data ranges for each movement. Sending data wirelessly make this system more portable with maximum distance between receiver and transmitter in the building are 20 metres. It using the assumption that receiver located 1 floor below and have 1 barrier.

Abstrak :
Smart driving dan eco driving saat ini menjadi isu yang penting dimana mengkaitkan cara berkendara dengan lingkungan, kenyamanan, dan keselamatan berkendara. Untuk mencapai target yang dituju dalam smart dan eco driving diperlukan pengukuran atau evaluasi terhadap behaviour kita dalam berkendara. Salah satu parameter yang mencerminkan behaviour kita dalam berkendara adalah driving cycle. Besaran yang diukur dalam driving cycle adalah kecepatan kendaraan setiap detiknya. Dalam riset ini, kecepatan kendaraan akan diukur menggunakan accelerometer pada smart phone. Dipilih menggunakan smartphone karena smartphone merupakan perangkat yang aplikasinya saat ini telah cukup luas tidak terbatas sebagai alat komunikasi saja karena telah dilengkapi dengan berbagai sensor dan feature lainnya. Kendala terbesar penggunaan accelerometer sebagai alat ukur kecepatan adalah timbulnya drift, hasil pengukuran dipengaruhi vibrasi dan gravitasi Bumi. Pada riset ini, digunakan metoda Fuzzy Logic untuk memberikan koreksi terhadap pembacaan accelerometer arah longitudinal yang dipengaruhi vibrasi dan drift dengan melihat besar vibrasi pada arah lateral dan vertikal. Degree of membership DOM dari setiap himpunan yang menggambarkan state gerak dan vibrasi kendaraan ditentukan berdasarkan sampling data yang kemudian dianalisis menggunakan distribusi Gauss sehingga besar peluang suatu percepatan menggambarkan suatu state atau keadaan dapat dimodelkan. Keakuratan dalam melakukan filter sangat tergantung desain filter yang kita lakukan meliputi meliputi range DOM pada setiap state atau himpunan yang didefinisikan, Membership Function, dan sebagainya. ......Smart driving and eco driving now become an important issue which they integrate environment, comfort, and safety riding. To achieve this condition, it is needed measurements or evaluations on our riding behaviour. One of parameters that describes our riding behaviour is driving cycle. The variable that is measured in driving cycle is the vehicle speed in each second. In this research the velocity of vehicle was measured by accelerometer on a smartphone. The choice of using smartphone in this research was because it is used for communication tool by many people and equipped by many sensors such as accelerometer, magnetometer, and many other features. The biggest obstacle of using accelerometer as velocity measuring instrument was the measurement result is affected by drift, vibration, and earth gravitation. In this research, Fuzzy Logic was used to give correction on accelerometer reading in longitudinal direction which is affected by vibration and drift by looking at vibration in the lateral and vertical direction. Degree of membership DOM in each set which describes vehicle's movements and vibrations is determined based on sampling data and analyzed with Gauss Distribution that probability of acceleration which describes a state can be modelled. The accuracy of filtering is depend on filter design that we have made that covers range DOM on each defined state or sets, Membership Function, etc.

Abstrak :
Peralatan Kesehatan yang ada di Indonesia masih mengandalkan teknologi yang di impor ataupun belum ada barangnya sama sekali. Sebagai contoh, pada masalah Parkinson, belum ada teknologi yang mampu untuk mendeteksi dan getaraan yang ada pada pasien. Sebagian besar penanganan medis untuk Parkinson Untuk itulah penulis ingin mengembangkan jam tangan untuk dapat mendeteksi Parkinson serta mampu untuk meredam gejala Parkinson dengan menggunakan motor DC Vibrator sebagai Aktuator untuk peredam. Penelitian yang dilakukan adalah mengambil data accelerometer dan gyroscope tangan getar kencang dan lambat dari penulis yang kemudian di proses data tersebut dengan deep learning pada keras beserta dengan perubahan-perubahan parameter. Setelahnya hasil dari pelatihan diinstall ke Arduino BLE 33. Setelah terinstall divais diuji coba apakah bisa mendeteksi getaran pada tangan.Dengan menggunakan jumblah data sebanyak 4800 menggunakan 3 layer dengan fungsi aktivasi ReLU, Training loss adalah 2,537 × dan Validation Loss 1,7315 × . Dari perbandingan data hasil training dan data testing untuk Train Accuracy dan validation accuracy pada Keras memiliki tingkat akurasi 1.0, yang bisa dianggap tinggi. Pada saat diuji coba kepada penulis, disaat penulis menggetarkan tangan dengan cukup kencang, divais mampu untuk mendeteksi getaran dan menggetarkan motor pada tangan.

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Health instruments in Indonesia are currently still using either imported technology or are not yet available locally. As for example, Parkinson's disease does not yet have the solution for detecting and supressing the tremor that happens in the patient's hand. For that reason, the writer intend to invent a device that could detect and suppress tremor called NASA-S.Research is conducted by taking the accelerometer and Gyriscope data of heavy and light vibration from the writer's hand and then being processed using deep learning by keras with changing and testing it's parameter variation. After the training, the result of the training will be installed in Arduino BLE 33. After the Installation, the device will be teste wether it can or not to perform the detection of arm vibration type. With using total 4800 number of data wiht 3 layer and activation function of ReLU, The result shows that The training loss of the model resulter 2.536e-04 and Validation loss 1.7315e-06. From the comparison of the training data and the testing data the Train accuracy and validation accuracy at Keras gived the Accuracy value of 1.0, which consideribly high. When tested at the hand of the writer, when the writer vibrate hand with enough vibration strength, the device could detect vibraton and vibrate the motor on writer's hand

Abstrak :
Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. According to World Health Organization (WHO), the percentage of health problems occurring in the world population, such as diabetes, heart problem, and high blood pressure rapidly increases from year-to-year. Hence, regular exercise, at least twice a week, is encouraged for everyone, especially for adults and the elderly. An accelerometer sensor is preferable, due to privacy concerns and the low cost of installation. It is embedded within smartphones to monitor the amount of physical activity performed. One of the limitations of the various classifications is to deal with the large dimension of the feature space. Practically speaking, a large amount of memory space is demanded along with high processor performance to process a large number of features. Hence, the dimension of the features is required to be minimized by selecting the most relevant feature before it is classified. In order to tackle this issue, the hybrid feature selection using Relief-f and differential evolution is proposed. The public domain activity dataset from Physical Activity for Ageing People (PAMAP2) is used in the experimentation to identify the quality of the proposed method. Our experimental results show outstanding performance to recognize different types of physical activities with a minimum number of features. Subsequently, our findings indicate that the wrist is the best sensor placement to recognize the different types of human activity. The performance of our work also been compared with several state-of-the-art of features for selection algorithms.