1,512 research outputs found

    慣性センサおよび力センサを用いた立ち上がり時の関節角度推定手法に関する研究

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    Standing-up motion from a chair is directly connected with walking and which is frequently performed every day. It is difficult for some elders because of the weakened function of muscles or motor. The training of standing-up motion and assisting the elderly with the standing-up motion from a chair is important to the elderly Quality of Life (QOL). Analysis of the posture parameters during standing up motion is useful for the physical therapists and care-giver in rehabilitation training or movement assist. The motion capture system can measure the movement of body posture in any direction precisely. However, it is difficult to use in daily life because of high cost and specific requirements for the space. And the use of motion capture system will give unpleasant feeling to users because many reflective makers are attached in the body. The purpose of this study is to develop a new estimation system, which can be used in daily life for angle estimation of extension phase during standing-up motion. This paper discusses the estimation system consist of: 1) the estimation of body joint angles and COG during extension phase; 2) the improvement of the proposed system for angle estimation. In 1), an estimation model was proposed that was able to estimate knee and ankle joint angles by combining angle and acceleration of trunk, which came from the inertial sensor attached to the chest of users during the extension phase. The estimate result of joint angle shows higher accuracy than previous research. In 2), in order to expand the use of proposed system and improve the estimation accuracy of proposed system, we estimated the initial angle of knee and ankle by combining foot pressure which measured by a force sensor plate before standing-up motion. The estimation model of initial lower limb angle shows high accuracy. It can be used for angle estimation of extension phase even though the initial knee and ankle joint angle were unknown.九州工業大学博士学位論文 学位記番号:生工博甲第317号 学位授与年月日:平成30年3月23日1 Introduction|2 Previous Researches|3 Angle Estimation of Extension Phase|4 Estimation of Initial Lower Limb Angle|5 Conclusion and Future Work九州工業大学平成29年

    Gait Analysis Using Wearable Sensors

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    Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications

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

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    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

    Pushing the limits of inertial motion sensing

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    Empowering and assisting natural human mobility: The simbiosis walker

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    This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

    Human Gait Analysis in Neurodegenerative Diseases: a Review

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    This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined

    Sensorized Tip for Monitoring People with Multiple Sclerosis that Require Assistive Devices for Walking

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    Multiple Sclerosis (MS) is a neurological degenerative disease with high impact on our society. In order to mitigate its effects, proper rehabilitation therapy is mandatory, in which individualisation is a key factor. Technological solutions can provide the information required for this purpose, by monitoring patients and extracting relevant indicators. In this work, a novel Sensorized Tip is proposed for monitoring People with Multiple Sclerosis (PwMS) that require Assistive Devices for Walking (ADW) such as canes or crutches. The developed Sensorized Tip can be adapted to the personal ADW of each patient to reduce its impact, and provides sensor data while naturally walking in the everyday activities. This data that can be processed to obtain relevant indicators that helps assessing the status of the patient. Different from other approaches, a full validation of the proposed processing algorithms is carried out in this work, and a preliminary study-case is carried out with PwMS considering a set of indicators obtained from the Sensorized Tip’s processed data. Results of the preliminary study-case demonstrate the potential of the device to monitor and characterise patient status

    Performance of the Intrac Wireless Activity Tracking System for the Afari Assistive Device

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    Afari is a mobility device that was designed to be more recreational, aesthetic, and functional outside than the typical mobility devices commonly used today such as walkers, crutches, and rollators. The Afari transfers weight from a user through the arm rests and enforces an upright posture while walking with correct adjustments to the arm rest height. In addition to assisting with walking or running, a sensor system fitted to the Afari device has been designed to analyze different aspects of activity tracking such as the dynamic loading applied to the arm rests, spatial-temporal gait parameters, speed, and distance. This includes various sensors, namely, load cells for each arm rest, an inertial measurement unit, and a speed and distance sensor that wirelessly transmit data via Bluetooth Low Energy (BLE) to either a smartphone or computer. The total distance, pitch angle, right and left loading on each armrest can be viewed in real time by the user. An algorithm was created in MATLAB to process all the raw data and compute cadence, stride length, average toe-off and heel strike angle, swing and stance time, and speed over the duration of active use. An Afari user can monitor these different aspects of their activity and adjust accordingly to potentially improve their balance or gait
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