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

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Posted September 20, 2019

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.

Free image via Pixabay

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.

Figure: Usage of the developed algorithm. Image credit: Fujitsu

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.

Background

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.

Issues

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.

Outcome

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.

Future Prospects

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.

Source: ACN Newswire

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