Risk of falling assessment on different types of ground using the instrumented TUG

Degradation of postural control observed with aging is responsible for balance problems in the elderly, especially during the activity of walking. This gradual loss of performance generates abnormal gait, and therefore increases the risk of falling. This risk worsens in people with neuronal pathologies like Parkinson and Ataxia diseases. Many clinical tests are used for fall assessment such as the Timed up and go (TUG) test. Recently, many works have improved this test by using instrumentation, especially body-worn sensors. However, during the instrumented TUG (iTUG) test, the type of ground can influence risk of falling. In this paper, we present a new methodology for fall risk assessment based on quantitative gait parameters measurement in order to improve instrumented TUG test. The proposed measurement unit is used on different types of ground, which is known to affect human gait. The methodology is closer to the real walking environment and therefore enhances ability to differentiate risks level. Our system assesses the risk of falling's level by quantitative measurement of intrinsic gait parameters using fuzzy logic. He is also able to measure environmental parameters such as temperature, humidity and atmospheric pressure for a better evaluation of the risk in activities of daily living (ADL)

PENGENALAN INDIVIDU BERDASARKAN GAIT MENGGUNAKAN SENSOR ACCELEROMETER

Setiap individu memiliki keunikan tersendiri dalam cara berjalan atau gait. Karena itu gait dapat digunakan untuk mengenali seorang individu. Sehingga gait dapat diimplementasikan sebagai biometrik. Accelerometer adalah sensor untuk mengukur dan mendeteksi getaran, ataupun untuk mengukur percepatan, yang juga bergantung pada arah atau orientasi. Sensor accelerometer sudah digunakan secara luas dikehidupan sehari-hari, terutama pada smartphone. Sehingga dimungkinkan untuk mengukur pergerakan individu saat berjalan menggunakan sensor accelerometer yang tertanam pada smartphone. Tugas akhir ini dilakukan pengenalan individu berdasarkan gait dengan menfaatkan sensor accelerometer yang tertanam pada smartphone. Untuk pengolahan data atau melakukan analisis pengenalan akan mengimplementasikan metode Mel-Frequency Cepstral Coefficient dan Hidden Markov Model. Metode Mel-Frequency Cepstral Coefficient akan digunakan untuk melakuakan ekstraksi ciri. Mel-Frequency Cepstral Coefficient digunakan untuk menghasilkan ciri gait yang direpresentasikan oleh koefisien MFCC. Sedangkan metode Hidden Markov Model digunakan untuk melakukan klasifikasi, dengan melakukan perhitungan dengan parameter matriks transisi, matriks observasi dan matrik

Activity Recognition using wearable computing.

A secure, user-convenient approach to authenticate users on their mobile devices is required as current approaches (e.g., PIN or Password) suffer from security and usability issues. Transparent Authentication Systems (TAS) have been introduced to improve the level of security as well as offer continuous and unobtrusive authentication (i.e., user friendly) by using various behavioural biometric techniques. This paper presents the usefulness of using smartwatch motion sensors (i.e., accelerometer and gyroscope) to perform Activity Recognition for the use within a TAS. Whilst previous research in TAS has focused upon its application in computers and mobile devices, little attention is given to the use of wearable devices - which tend to be sensor-rich highly personal technologies. This paper presents a thorough analysis of the current state of the art in transparent and continuous authentication using acceleration and gyroscope sensors and a technology evaluation to determine the basis for such an approach. The best results are average Euclidean distance scores of 5.5 and 11.9 for users\u27 intra acceleration and gyroscope signals respectively and 24.27 and 101.18 for users\u27 inter acceleration and gyroscope activities accordingly. The findings demonstrate that the technology is sufficiently capable and the nature of the signals captured sufficiently discriminative to be useful in performing Activity Recognition

Comparing auditory, visual and vibrotactile cues in individuals with Parkinson’s disease for reducing risk of falling over different types of soil

Introduction: Several researches have demonstrated the positive benefits of auditory and visual cueing in the gait improvements among individuals with Parkinson’s disease (PD). However, few studies have evaluated the role of vibrotactile cueing when compared to auditory and visual cueing. In this paper, we compare how these stimuli affect the risk of falling while walking on six types of soil (concrete, sand, parquet, broken stone, and two types of carpet). Methods: An instrumented Timed Up and Go (iTUG) test served to evaluate how audio, visual and vibrotactile cueing can affect the risk of falling of elderly. This pilot study proposes twelve participants with PD (67.7 ± 10.07 years) and nine age-matched controls (66.8 ± 8.0 years). Both groups performed the iTUG test with and without cueing. The cueing frequency was set at 10% above the cadence computed at the lower risk level of falling (walking over the concrete). A computed risk of falling (ROFA) index has been compared to the TUG time (total TUG duration). Results: The index for evaluating the risk of falling appears to have a good reliability (ICC > 0.88) in this pilot study. In addition, the minimal detectable change (MDC) suggests that the proposed index could be more sensitive to the risk of falling variation compared to the TUG time. Moreover, while using the cueing, observed results suggest a significant decrease in the computed risk of falling compared to ‘without cueing’ for most of types of soil especially for deformable soils, which can lead to fall. Conclusion: When compared to other cueing, it seems that audio could be a better neurofeedback for reducing the risk of falling over different walking surfaces, which represent important risk factors for persons with gait disorder or loss functional autonomy

Real-World Smartphone-based Gait Recognition

As the smartphone and the services it provides are becoming targets of cybercrime, it is critical to secure smartphones. However, it is important security controls are designed to provide continuous and user-friendly security. Amongst the most important of these is user authentication, where users have experienced a significant rise in the need to authenticate to the device and individually to the numerous apps that it contains. Gait authentication has gained attention as a mean of non-intrusive or transparent authentication on mobile devices, capturing the information required to verify the authenticity of the user whilst the person is walking. Whilst prior research in this field has shown promise with good levels of recognition performance, the results are constrained by the gait datasets utilised being based upon highly controlled laboratory-based experiments which lack the variability of real-life environments. This paper introduces an advanced real-world smartphone-based gait recognition system that recognises the subject within real-world unconstrained environments. The proposed model is applied to the uncontrolled gait dataset, which consists of 44 users over a 7–10 day capture – where users were merely asked to go about their daily activities. No conditions, controls or expectations of particular activities were placed upon the participants. The experiment has modelled four types of motion normal walking, fast walking and down and upstairs for each of the users. The evaluation of the proposed model has achieved an equal error rate of 11.38%, 11.32%, 24.52%, 27.33% and 15.08% for the normal, fast, down and upstairs and all activities respectively. The results illustrate, within an appropriate framework, that gait recognition is a viable technique for real-world use

Method to determine physical properties of the ground

The method can determine physical properties of the ground stepped upon by a user wearing a footwear incorporating an accelerometer, and includes: receiving a raw signal from the accelerometer during at least one step being taken by the user on the ground; identifying, in the received raw signal, at least one characteristic signature; associating the at least one characteristic signature to physical properties of the ground; and generating a signal indicating the physical properties based on said association. The generated signal can further be used to advise a user of a risk of falling based on at least the physical properties of the ground

Klasifikasi Sinyal EMG Pada Otot Tungkai Selama Berjalan Menggunakan Learning Vector Quantization - Classification Of EMG In Lower Limb Muscle During Walking Using Learning Vector Quantization

Sinyal electromyography (EMG) adalah aktifitas listrik yang terjadi pada lapisan otot selama adanya gerakan aktif. Gaya berjalan seseorang akan dipengaruhi oleh struktur tulang dan otot sehingga gaya berjalan tersebut adalah unik. Keunikan ini dapat digunakan untuk data biometrik. Dalam penelitian ini, kami akan melakukan klasifikasi data EMG untuk 8 otot tungkai selama berjalan yaitu Rectus Femoris, Vastus Medialis, Vastus Lateralis, Bicep Femoris, Semitendinosus, Gastrocnemius Medialis, Gastrocnemius Lateralis, dan Tibialis Anterior. 6 orang subyek sehat ditempelkan 8 elektroda EMG pada otot tungkai dan diminta untuk berjalan secara normal sesuai dengan kecepatan masing-masing di laboratorium gait. Masing-masing subyek berjalan sebanyak 1 siklus berjalan (gait cycle) dan 3 kali pengambilan data (walking trial). Total data pengambilan adalah sebanyak 18 buah dataset untuk analisis dan klasifikasi. Metode graph feature extraction dan principal component analysis digunakan untuk mengekstraksi fitur data EMG dari 8 otot tungkai selama berjalan. Metode learning vector quantization (LVQ) digunakan untuk mengklasifikasi data EMG berdasarkan subyek. Metode pembelajaran dan pengujian data pada jaringan LVQ menggunakan metode validasi silang (cross validation). Akurasi klasifikasi rata-rata menggunakan metode graph feature extraction diperoleh sebesar 88.89% dan metode PCA diperoleh sebesar 66.67%. Dari hasil ini menunjukkan bahwa sinyal EMG selama berjalan dari 8 otot tungkai dapat digunakan sebagai identitas biometrik gait. ======================================================================================================================== Electromyography (EMG) signal is an electrical activity that occurs in the muscle layer during active motion. The way people walking is defined by the structure of individual muscle and bones so that the way of walking is unique and must be able to used in biometric data. In this study, we classified the EMG data dari 8 lower limb muscle during normal walking test (Rectus Femoris, Vastus Medialis, Vastus Lateralis, Bicep Femoris, Semitendinosus, Gastrocnemius Medialis, Gastrocnemius Lateralis, and Tibialis Anterior). Six healthy volunteer were involving in this study by walking in GaitLab with 8 EMG electrodes attached on their muscle. Each volunteer performed one gait cycle and 3 walking trial. So in total 18 EMG dataset were analized for classification. Graph feature extraction and principal component analysis method was used to extract the feature of EMG data of all 8 muscle during walking. Learning Vector Quantization (LVQ) was used to classify the EMG data based on subject. Training and testing method in LVQ network used cross validation (CV). The average accuracy of classification using graph feature extraction method is 88.89% and using PCA method is 66.67%. In the result show that EMG data during walking of 8 lower limb muscles can be used to identity of gait biometric