http://ieeexplore.ieee.org/iel5/5/31324/01456901.pdf

Microelectronic circuits

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions.

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Real-time human ambulation, activity, and physiological monitoring: taxonomy of issues, techniques, applications, challenges and limitations.

Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. The influence of various important hyper-parameters such as number of convolutional layers and kernel size on the performance of CNN was monitored. Experimental results indicate that CNNs achieved significant speed-up in computing and deciding the final class and marginal improvement in overall classification accuracy compared to the baseline models such as Support Vector Machines and Multi-layer perceptron networks.

/pdf/human-activity-recognition-with-inertial-sensors-using-a-e3bg1umi3r.pdf

Human activity recognition with inertial sensors using a deep learning approach

The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

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Deep Learning for Monitoring of Human Gait: A Review

We demonstrate accurate spatio-temporal gait data classification from raw tomography sensor data without the need to reconstruct images. This is based on a simple yet efficient machine learning methodology based on a convolutional neural network architecture for learning spatio-temporal features, automatically end-to-end from raw sensor data. In a case study on a floor pressure tomography sensor, experimental results show an effective gait pattern classification F -score performance of 97.88 $\pm$ 1.70%. It is shown that the automatic extraction of classification features from raw data leads to a substantially better performance, compared to features derived by shallow machine learning models that use the reconstructed images as input, implying that for the purpose of automatic decision-making it is possible to eliminate the image reconstruction step. This approach is portable across a range of industrial tasks that involve tomography sensors. The proposed learning architecture is computationally efficient, has a low number of parameters and is able to achieve reliable classification F -score performance from a limited set of experimental samples. We also introduce a floor sensor dataset of 892 samples, encompassing experiments of 10 manners of walking and 3 cognitive-oriented tasks to yield a total of 13 types of gait patterns.

Deep Neural Networks for Learning Spatio-Temporal Features From Tomography Sensors

We report on the photonic variant of the previously introduced Guided-Path Tomography (GPT), by demonstrating a system for footstep imaging using Plastic Optical Fiber (POF) sensors. The 1 m × 2 m sensor head is manufactured by attaching 80 POF sensors on a standard commercial carpet underlay. The sensing principle relies on the sensitivity of POF to bending, quantified by measuring light transmission. The Photonic GPT (PGPT) system, comprising the sensor head with processing hardware and software, covered by a mass-production general-purpose carpet top, successfully performs footstep imaging and correctly displays the position and footfall of a person walking on the carpet in real time. We also present the implementation of fast footprint “center of mass” calculations, suitable for recording gait and footfall. A split-screen movie, showing the frame-by-frame camera-captured action next to the reproduced footprints is included in the electronic version of this paper.

Intelligent Carpet System, Based on Photonic Guided-Path Tomography, for Gait and Balance Monitoring in Home Environments

We report on the concept, sensing principles, hardware and software implementation, as well as the performance of a demonstrator smart carpet, suitable for integration into the everyday environment. The deformation-induced transmission losses through strategically placed optical fiber sensing elements are measured and images of the footprints of objects in the imaged scene are reconstructed by an original method suitable for the limited datasets acquired. We discuss the potential for a very compact system, easy to integrate with the living environment and the outside world.

Smart carpet for imaging of objects' footprint by photonic guided-path tomography

Photonic Guided-Path Tomography with Fibre Transducers

Surat merupakan salah satu sarana komunikasi penting yang digunakan oleh SMA Negeri 2 Sukoharjo sebagai penyedia informasi. Pada SMA Negeri 2 Sukoharjo, pengelolaan surat belum dilakukan secara komputerisasi. Proses tersebut akan mengalami kendala seperti kesulitan mencari surat sewaktu-waktu, hilangnya surat dan membutuhkan penyimpanan besar. Adapun tujuan dilakukan penelitian ini yakni membangun sebuah sistem informasi arsip berbasis website menggunakan framework codeigniter. Metode penelitian yang diterapkan meliputi observasi dan wawancara. Metode pengembangan yang digunakan yaitu model  waterfall yang memiliki tahapan secara sistematis berurutan dimulai dari analisis kebutuhan hingga perawatan sistem. Penelitian ini menghasilkan beberapa bagian menu untuk menambah data surat, mengubah data surat, mengunggah dokumentasi surat, melakukan pencarian surat dan mencetak laporan yang memudahkan pengelola dalam aktivitas surat-menyurat. Dalam pengujian sistem, penulis menggunakan black box testing dan SUS (System Usability Scale) dengan hasil nilai rata-rata yang diperoleh 83,5. Hal ini membuktikan bahwa sistem informasi ini memiliki nilai usability acceptable dan dapat diterapkan pada SMA Negeri 2 Sukoharjo.

/pdf/sistem-informasi-arsip-surat-pada-sma-negeri-2-sukoharjo-2izqb44u42.pdf

Sistem Informasi Arsip Surat Pada SMA Negeri 2 Sukoharjo Menggunakan Framework Codeigniter

Kabupaten Boyolali adalah salah satu kabupaten dari 35 Kabupaten/Kota di Provinsi Jawa Tengah yang pro-investasi dan memiliki kurang lebih unit yang terdiri dari industri, mikro, kecil dan menengah. Kondisi saat ini, pengelolaan Indutri Kecil Menengah Pemerintah Kabupaten Boyolali masih melakukkan pendataan secara konvensional. Meskipun jarak tempuh yang jauh, Dinas Perdagangan dan Perindustrian harus merekap data dengan cara mendatangi setiap kecamatan-kecamatan di Kabupaten Boyolali. Selain itu, belum adanya media informasi Industri Kecil Menengah Kabupaten Boyolali yang dapat diakses oleh masyarakat umum. Penelitian ini bertujuan membuat Sistem Informasi Industri Kecil Menengah Pemerintahan Kabuoaten Boyolali Berbasis Website untuk memudahkan dinas mendapatkan data-data industri, masyarakat umum yang memiliki industri dapat mendaftar secara online agar dapat mengajukan bantuan secara online, serta menjadikan sistem ini sebagai media promosi industri. Sistem Informasi ini menggunakan framework laravel dengan bahasa pemrograman PHP, dan database MySQL. Metode pengembangan perangkat lunak yang digunakan adalah waterfall, dalam perancangan dan pembuatan meliputi tahap analisis kebutuhan, desain aplikasi, pengkodingan aplikasi, pengujian, dan perawatan. Berdasarkan hasil uji black box, sistem mempunyai menu kelola data industri, kelola data member, kelola rekap industri, kelola pengajuan bantuan serta sistem ini sebagai sarana media informasi seputar Industri Kecil Menengah. Berdasarkan pengujian SUS, sistem mendapat skor 70,58 yang berarti sistem termasuk dalam kategori baik dan dapat diterima.

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N Nurgiyatna

Papers

Intelligent Carpet System, Based on Photonic Guided-Path Tomography, for Gait and Balance Monitoring in Home Environments

Smart carpet for imaging of objects' footprint by photonic guided-path tomography

Photonic Guided-Path Tomography with Fibre Transducers

Sistem Informasi Arsip Surat Pada SMA Negeri 2 Sukoharjo Menggunakan Framework Codeigniter

Sistem Informasi Industri Kecil Menengah Pemerintahan Kabupaten Boyolali Berbasis Website