6th International Forum on Advanced Technologies
March 9th-10 th, 2020,Tokushima, JapanClassification of Gait Data from Daily Walking
Using Long Short-Term Memory
Toshiya Matsunashi1* , Yoko Uwate1 , Donald Y.C. Lie2 and Yoshifumi Nishio1
1
Tokushima University, Tokushima 770-8501, Japan* E-mail: matsunashi@ee.tokushima-u.ac.jp
2 Texas Tech University, 2500 Broadway, Lubbock, TX, 79409, USA
1. Introduction
Walking is a natural means of movement in our lives, and it is one of the easiest exercises for human beings.
If a person cannot perform stable walking, it may be difﬁcult for him/her to lead an ordinary life and the risk ofvarious illness rises [1]. Falls are very common and yet dangerous among the elderly population and are amajor health concern. With the increase of elderly population all over the world, objective fall risks evaluations
from various walking gaits for the geriatrics can be very attractive and useful. Gait analysis using a lightweightbody-worn sensor is a convenient and inexpensive method that potentially allows for easy and quantitative
assessment of fall risks of the geriatric population.
In this study, older adults who each worn a gait belt on the lower back were evaluated using data obtained
from lightweight sensors including a triaxial accelerometer and a triaxial gyroscope. Algorithms such assupport vector machine (SVM) [2]-[3], artiﬁcial neural network (ANN) [3] and Long Short-Term Memory(LSTM) [4] were used to classify fallers vs. non-fallers.

2. Method
In the dataset we used for this work, 71 community-living elderly people (mean age = 78.3 ± 4.71 years,range = 65 87 years, mean height = 1.62 ± 0.07m, mean weight: 71.98 ± 12.88 kg) were tested. Subjects are
divided into faller or non-faller based on self-reporting of past fall experience. If the subject falls more than
twice in the past year, that person is classiﬁed as a faller, otherwise they were considered non-fallers. Subjects
attached the gait belt on lower part of waist as shown in Fig. 1 and took various tests while walking. Amongthem, the data from the following two experiments were used for this work:1) 1-minute walk in lab: Walk in the lab at a personal selectedcomfortable speed with the gait belt on lower back.
2) 3-day long term recording: After a 1-minute walk testin the lab, subjects were required to spend 3-days
wearing the gait belt, except for taking shower, etc.
but the reason for the removal must be reported.)

LSTM is one of the models for sequential data analysis thatappeared as an extension of Recurrent Neural Network (RNN).
LSTM is realized by replacing the unit in the middle layer of
RNN with a memory, and a block with three gates that are
called LSTM block as shown in Fig. 2. In RNN, in order tomaintain the storage of input data, the middle layer has a loop.
This allowed RNN to include the previous data in the judgment.
However, the basic RNN had various issues such as thegradient loss problem, and its learning is not very effective. TheFig. 1 Indicating where the gait belt wasattached to subjects (at L5; lower back).

Fig. 2 Learning structure of LSTM

6th International Forum on Advanced Technologies
March 9th-10 th, 2020,Tokushima, Japan
LSTM block includes a forget layer, a retention layer, and so on. The feature of LSTM is that it can learn
long-term dependencies that were difﬁcult to learn in the RNN and can solve the gradient loss problem. In
other words, it can be divided into necessary information and unnecessary information, then proceed withlearning using only the necessary information. Thus, LSTM has attracted the attention of its learningsuperiority on long-term sequential data that was difﬁcult to predict in RNN.
3. Results and discussion
In this study, we classiﬁed fallers vs. non-fallers using SVM, ANN and LSTM as algorithms. However,since SVM and ANN are not directly suitable for classiﬁcation of sequential data, the data obtained as input
data was modiﬁed and used. Range, standard deviation (STDEV) and Range + standard deviation (STDEV)
were used as input data when we classify using SVM and ANN. Range is the maximum value minus theminimum value of the signal obtained on each axis. The reason for using the STDEV is that the fallers’
movement in their daily living is likely to be unstable, and the variation in data might become large, and it isthus reasonable that larger STDEV could differentiate themselves from the non-fallers. Table 1 shows theresults when we use SVM and ANN as classifying algorithms; 70% was used as training data and 30% as test
data. As can see from the Table 1, not all features have achieved very high accuracy. In particular, when only
STDEV was used in the input data, it showed the same or lower accuracy than when using Range alone. Thisindicates that the variability of the data may not be signiﬁcantly different between fallers and non-fallers. In
the case of Range + STDEV, a high accuracy of 71.67% was obtained when 1-min lab walk was classiﬁedusing ANN, but this did not improve accuracy for other cases. For the 1-min walk, ANN had higher accuracy
than SVM in all features by 2% to 16%. For the 3-day recording, ANN has higher accuracy than SVM in two
features by 5%-6%, but in the case of STDEV it has 4% lower accuracy. Although the overall accuracy is not
so high as shown in Table 1, it is suspected that the cause is at least partially due to that classiﬁcation wasperformed by ignoring the time factor despite of the time series sequential gait data we are given. In the case ofRange, in particular, it uses the maximum and minimum values in tests that can be for a very long time, so it
may not be very suitable for accurate classiﬁcation.

Table.1 Overall classification accuracy of fallers vs. non-fallers using ANN and SVM for both tests.

Next, analysis was performed using LSTM. Note in the case of the above ANN and SVM, the obtained gait
data was transformed (features extracted) to make it easier to classify, but in the case of LSTM, it wasclassiﬁed using the obtained raw gait data (all six axes data were used as input data) without deformation. Thisis done to preserve the continuous time series component of the recorded data by not transforming it into range,etc. The ﬁnal classiﬁcation accuracy was 72.72% for the 1-min lab walking, and a very high 81.81% for the 3days of recording, as shown in Table 2. Compared to ANN and SVM, LSTM gave that by using LSTM, it canhelp to predict future results from past data. From the above analysis and results, it is clear that we can classify
fallers vs. non fallers with high accuracy by classifying using LSTM rather than using ANN or SVM. The ﬁnal
accuracy after learning is summarized in Table 2.
Table.2 Final accuracy with LSTM.

6th International Forum on Advanced Technologies
March 9th-10 th, 2020,Tokushima, Japan
However, LSTM is not full of good points only. Of course, when forecasting using LSTM, the more data wehave, the longer it will take to calculate. Accuracy is important for fall risks prediction, but the speed ofcalculation may also be important for performing a fall risks assessment. Even if high accuracy is obtained,
real-time analysis is not feasible if classiﬁcation takes more than a few minutes. Therefore, it is necessary tocompare the calculation speed of each classiﬁer. Fast calculation speeds lead to quick feedback from the doctor
to the patient. Table 3 summarizes the calculation speed of each classiﬁer. SVM takes only about 1 second tocalculate on a typical PC (Windows 10 Pro, Intel core i5-7300U, 2.60GHz, 2.71GHz, 8.00GB, 64 bit, 64-bit
operating system), and ANN takes about 7 seconds. In the case of LSTM, the 1-min lab walk classiﬁcation
takes 27249 seconds and the 3-day recording classiﬁcation takes 39815 seconds. As a result, although LSTM
has higher accuracy than SVM and ANN, it has the disadvantage that it takes much more calculation time,even though with higher accuracy. It turned out that SVM with a simple structure is very fast and suitable forreal-time gait analysis. Which classiﬁer is best for fall risks evaluation is likely to vary depending on thespeciﬁc application. If we do not need immediate feedback and can use a fast server to calculate, we shouldchoose to spend more time making predictions with LSTM as it is considerably more accurate.
Table.3 Summary of each classifier’s speed when different input features are used.

4. Conclusion
The results presented in this study using ANN, SVM, and LSTM suggest that we can classify fallers vs.
non-fallers with considerably higher accuracy using LSTM than ANN or SVM. In this study, we used datacollected from the 1-min walking and the 3-day daily living activities for 71 subjects and achieved aclassiﬁcation accuracy of 72.72% and 81.81%, respectively. These results suggest that long-term monitoring
and the analysis on the temporal changes in gait behaviors can be a rather powerful and novel technique forquantitatively evaluating fall risks on the geriatric patients performing their average daily living activities.

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