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  • Fujitsu Develops Digitization Technology to Quantify Various Walking Characteristics Resulting from Diseases

    Published on September 18, 2019

    KAWASAKI, Japan:  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.

    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.