Fujitsu Develops Digitization Technology to Quantify Various Walking Characteristics Resulting from Diseases
In monitoring the gait of patients, helps healthcare professionals quantify movements and record recovery processes
KAWASAKI, Japan, Sep 18, 2019 - (JCN Newswire) - Fujitsu Laboratories Ltd. and Fujitsu Limited have developed a technology to digitize and quantify the walking patterns of patients whose movements vary due to the impact of different diseases.
|Figure: Usage of the developed algorithm
Medical professionals can identify the symptoms of patients by observing their way of walking. However, it is difficult to digitize symptoms as there are numerous walking characteristics that differ depending on the type and severity of the disease, and as of now, physiotherapists conduct visual inspections in most cases. Now, Fujitsu has developed a technology to automatically and accurately quantify factors such as the swing time and stance time(1) of the right and left leg as well as the difference between the movements of both legs. In the new development, feature points at the time of movement change will be determined using signal waveforms emitted from commercially available gyro sensors attached to the patients' ankles.
It is said that various symptoms such as musculoskeletal, neural and cardiovascular conditions affect the walking characteristics of patients. The new technology will enable healthcare professionals to quantify the gait of patients walking under the influence of such conditions, and as a result, they will be able to record recovery processes and help with the remote monitoring of patients, thereby improving the efficiency of medical services.
In the medical field, it is essential to analyze the walking of patients to examine their changing symptoms and recovery status. In fact, it is well known that symptoms such as musculoskeletal, neural and cardiovascular conditions cause walking abnormalities. Accordingly, there was a demand for a walking analysis technology that could digitally capture the same information as physiotherapists in detecting early signs of disease symptoms.
A number of methods based on machine learning and rule-based algorithms have been proposed as conventional techniques for comparing and analyzing walking characteristics as quantitative data, and have attracted the attention of healthcare professionals. Nonetheless, physiotherapists work with patients diagnosed with a wide range of diseases, and the impact on their walking patterns differ significantly depending on such factors as the nature of the disease, its severity, and the location of disabled areas. Therefore, conventional techniques could not quantify various walking characteristics with high accuracy, as they could only analyze a limited number of walking patterns or were unable to prepare sufficient walking data for learning.
About the Newly Developed Technology
Fujitsu has developed a technology that can quantify the characteristics of various walking styles based on signals from gyro sensors attached to the patient's ankles. This technology makes use of the newly developed model based on the law of motion, such as the relationship between the movements of the left and right legs during walking and how different walking characteristics transition over time, detecting feature points and assigning meaning to the signal waveform emitted from the gyro sensors. In this way, the signal of the walking step alone can be clearly identified, and the feature points of the walking step, when the heel touches the ground or when the toe is off the ground, can be recognized regardless of the walking method. By measuring these feature points, walking characteristics such as stride length and swing time can be quantified with high accuracy.
Utilizing a commercially available gyro sensor, the new technology evaluates various ways of walking, including 9 types of walking abnormalities (walking in short steps, circumduction, shuffling, etc.), enabling an accurate calculation of multiple walking characteristics. Specifically, the automatic recognition accuracy of the walking segment for walking motions was 96.5% and the extraction error of stride time (sum of stance time and swing time) was 1.8%. In other words, the new technology reduced the measurement error up to 1/3 times compared to conventional commercial products that require manual input of walking section.
Fujitsu will continue to develop the new digitization technology for the utilization of walking observation data by medical professionals as well as for the remote monitoring of home patients who are rapidly increasing in number.
(1) Swing time and stance time of the left and right legs The period in which one leg does not touch the ground during one walk cycle is called swing time, and the period in which one leg stays on the ground is called stance time.
About Fujitsu Laboratories
Founded in 1968 as a wholly owned subsidiary of Fujitsu Limited, Fujitsu Laboratories Ltd. is one of the premier research centers in the world. With a global network of laboratories in Japan, China, the United States and Europe, the organization conducts a wide range of basic and applied research in the areas of Next-generation Services, Computer Servers, Networks, Electronic Devices and Advanced Materials. For more information, please see: http://www.fujitsu.com/jp/group/labs/en/.
About Fujitsu Ltd
Fujitsu is the leading Japanese information and communication technology (ICT) company offering a full range of technology products, solutions and services. Approximately 130,000 Fujitsu people support customers in more than 100 countries. We use our experience and the power of ICT to shape the future of society with our customers. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.9 trillion yen (US$35 billion) for the fiscal year ended March 31, 2020. For more information, please see www.fujitsu.com.
Fujitsu Laboratories Ltd.
Digital Innovation Core Unit
E-mail: [email protected]
Public and Investor Relations
Source: Fujitsu Ltd
Sectors: Electronics, Enterprise IT
Copyright ©2020 JCN Newswire. All rights reserved. A division of Japan Corporate News Network.
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