情報処理学会 IoT 行動変容学研究グループ（SIG-BTI）

Stand-up Activity Prediction from Textile Pressure Sensors
Tahia Tazin†1
Kyushu Institute of Technology

John Noel Victorino†2
Kyushu Institute of Technology

Yu Enokibori†4
Nagoya University

Sozo Inoue†5
Kyushu Institute of Technology

1. Abstract

ment of the elderly all the time, especially when they areThis paper presents a stand-up activity prediction sys-

sitting in a wheelchair. Accidents are more likely to oc-

tem using textile pressure sensors to reduce unexpected
falls. To achieve this, we equipped a wheelchair with tex-

cur when they try to stand up from the wheelchair. Also,

tile pressure sensors to collect data on wheelchair users’is diﬃcult for a nurse to monitor these issues at all times.

activities, such as sitting, exercising, and trying to standOn the other hand, in addition to the increase in the

many times these accidents occur in a location where it

In a lab environment, we collected activity data

elderly population in Japan, dementia patients are also

from three users to forecast sudden attempts to standincreasing rapidly. Based on data analysis results fromup, which may lead to falls.

research by Kyushu University, it is estimated that byup.

In this study, the eﬀectiveness of the personalized

2025, 1 in 5 adults over 65 will be aﬀected by demen-model and the leave-one-subject-out (LOSO) method

tia [1]. Movement and balance-related brain regions canwere compared. Also, we compared the performance withbe impacted by dementia. Many people with Alzheimer’s

diﬀerent feature sets. With four general statistical fea-

disease or another kind of dementia eventually lose thetures, we achieved an 84% accuracy and an 81% F1-score

capacity to move and carry out daily duties. Also, pa-

to predict trying to stand-up activity in the one-person
leave-out method. Then, the accuracy and F1-score in-tients with dementia often forget their current state. As
a result, they can fall when they suddenly try to stand up

creased to 86% and 83%, respectively with six additionalfrom their sitting position. In nursing homes, people withfeatures. Finally, personalized models produced the high-dementia experience fall an average of 4.05 times a year

est accuracy and F1-score of 91% and 94%, respectively

compared to 2.33 times annually for other patients [2]. To

for the stand-up activity.

prevent these accidents, it is vital to establish the proper

Overall, the results demonstrate the potential of pre-provisions after completing assessments on the relevant

dicting wheelchair activities using textile pressure sen-patients. Using a textile pressure sensor-based predic-

sors, contributing to a lower risk of falling from thetion system that alerts caregivers when patients attemptwheelchair.

to stand up from their wheelchairs is one solution to thisissue.
Electronic textile structures have attracted much at-

2. Introduction

tention over the past 20 years in research and devel-

Along with longevity, labor shortages are becoming aopment due to their innate softness, breathability, andsevere problem in today’s society, especially in nursing

ﬂexibility. These features produce a satisfying platform

homes. Caregivers are not growing at the same rate as thefor sensing various inputs like pressure, temperature, andaging population, making accurate monitoring diﬃcult.

strain [3, 4, 5]. However, to support the daily lives of

It is not possible for a caregiver to monitor every move-the elderly, professional life-logging technologies have also
started to arise, and context-aware solutions with sensor

Stand-up Activity Prediction from Textile Pressure Sensors

devices are being introduced. Attaching several sensors†1

to the body makes it possible to obtain much information

†2
†4
†5

Tahia Tazin, Kyushu Institute of Technology
John Noel Victorino, Kyushu Institute of Technology
Yu Enokibori, Nagoya University
Sozo Inoue, Kyushu Institute of Technology

about human movement and activity. Wearable technology should be incorporated into daily life use products,
1

情報処理学会 IoT 行動変容学研究グループ（SIG-BTI）

as attaching several devices may involve labor or addi-

activities, or posture detection, are done with diﬀerent

tional expense. At the same time, having many sensors attached to the body can feel unsteady for elderly

sensors, such as accelerometers, gyroscopes, force, andor dementia patients. As a result, we concentrated oncushions are unobstructed, they have been used in vari-wheelchair cushions since they are commonly used for el-ous related studies to track the comfort and well-being,

derly and dementia patients and can be used to monitor

posture, fatigue, and physical activity of regular chair andbody pressure and postural position, allowing us to getwheelchair users [6, 7]. On the other hand, researchers

many data on human behavior.

have proposed many methods by which it is possiblepressure sensors. Because pressure sensor-based smart

If activity were predicted from the pressure sensors

to detect the fall of the elderly. In this case, also, fallbuilt into cushions, it would be possible to naturally sense

detection is done by various wearable sensors and cam-human behavior during movement. This capability would

eras [8, 9, 10].

be helpful in many situations, not only in daily life but

Xu et al. [11] proposed the smart textile-based sensor

also in nursing homes or hospitals. Also, such cushion-in the cushion to recognize human sitting postures. The

type technology would become comfortable for wheelchairbinary representation of a gray-scale image was evaluatedusers who are not used to charging or discharging devices,using data on the binary pressure distribution that had

so they might be employed in technologies used for de-been collected. By employing dynamic temporal warping

mentia patients or the elderly.

to analyze the pressure distributions, they could pinpoint

sensors for fall detection; however, the sudden eﬀort ofspeciﬁc postures of the sitting position. Also, Kamiya et
al. [12] used an 8 x 8 pressure sensor matrix placed in

the elderly to stand up has never been predicted. Also,a chair cushion to recognize the sitting postures. They

the ideal sensor conﬁguration for predicting states must

used Support Vector Machine (SVM) with a radial basis

be thoroughly researched. The rate of unexpected falls

function for classiﬁcation.

Currently, there are many methods of using wearable

will drastically decrease if we can investigate the postureOn the other hand, Khan et al. proposed a camera-

movement before standing up and alert the nurses via thealarm system.

based technique for an abnormal human activity detection system [13]. They proved that this method could beThis paper proposes a method for predicting body pos-used for elderly care using Kernel discriminant analysis

tural movements before standing up from a wheelchair

and the Hidden Markov model. Similarly, Pavan et al.

using e-textile pressure sensors embedded in wheelchair

analyzed the video signals by applying manifold learning

cushions. We designed a supervised machine learning

to identify human activities [14].

model to address the trying-to-stand-up activity predic-

Sensor systems with early bed-leaving behavior pre-tion system utilizing textile pressure sensor data. In a lab

dictions have been researched to prevent fall accidents.

setting, we started by collecting a series of raw pressure

Asano et al. [15] proposed a depth camera-based detec-

data. The instances of raw data were then aggregated,tion system to avoid fall-related incidents. After optimiz-with each instance labeled with the activity that took

ing parameters and motion variables with distinct par-place while the data was being collected. Then, using

ticipants’ body size, location, and orientation, they usedmachine learning techniques, we created predictive mod-SVM to recognize bed-leaving behavior patterns. Afterels for stand-up activity prediction systems.

68 iterations, their experimentally produced result had a

The rest of the paper is organized as follows: Section

92.65% recall rate. On the other side, the authors in [16]3 discusses the related research, then Section 4 describes

provided the bed-leaving behavior identiﬁcation system.

the details of the proposed method, Section 5 analyzes
the results, and ﬁnally, Section 6 summarizes the entire

They employed bed pad sensors on a bed and a rail sensor placed inside a rail to detect bed leaving behavior.

paper.

Additionally, they generated a benchmark dataset of ten
subjects’ continuous and discontinuous behavioral pat-

3. Related Research
Nowadays, detecting the posture and daily activitiesof the elderly is a hot research topic worldwide. Theseterns. According to the experimentally collected results,
Random forest achieved 91.1% accuracy in their benchmark dataset.
However, diﬀerent bed-leaving sensors are now widely2情報処理学会 IoT 行動変容学研究グループ（SIG-BTI）

available from manufacturers. For instance, clip sensors,
infrared (IR), and mat sensors are often used in hospitals
and nursing homes. The most practical method currently
available, clip sensors, is simple to use, but because they
are fastened directly to the patient’s nightwear, care recipients are conﬁned by sensor wires. Furthermore, acare recipient’s neck could get wrapped around a sensor
wire. Consequently, the usage of clip sensors has recently
been discouraged. Due to the lack of constraints, matsensors which are more aﬀordable than clip sensors, arefrequently utilized in clinical settings. One drawback ofmat sensors is that they take a long time to detect and
図1

react when a care recipient places their feet on the matwhile seated at the bed terminal.

Figure 1

e-textile 圧力センサを搭載した車いす
Wheelchair with e-textile pressure sensors

As clip and mat sensors have some drawbacks, andwearable sensors are always diﬃcult to use for older
adults, we proposed a textile pressure sensor-based standup activity prediction system that can prevent any accidental event in a nursing home. Our proposed method
has been discussed in the following section.

4. Proposed Method
The smart wheelchair contains an e-textile pressure
sensor in the seat cushion and the backrest. These sensor

図2

織物圧力センサを用いた転倒防止システムのアーキ

data can be used to recognize postural movement activ-

テクチャ提案

ities such as sitting, exercising, and trying to stand up.

Figure 2

The pressure sensors must be connected to a Raspberry

sensor-based fall prevention system

Proposed architecture of the textile pressure

Pi via Bluetooth to get the raw activity data. When thedevice is connected, it can even detect very slight postural movement. Figure 1 shows the overview of our smart
wheelchair. In this section, we discuss our proposed system architecture.
4.1. System Architecture

this in mind, we have proposed a system that can givean alarm when someone is trying to stand up from theirsitting position.
Figure 2 shows our proposed system architecture. Seatand backrest pressure sensors gather pressure data whena person is seated in a wheelchair. An older person or

As it is a textile pressure sensor-based smart wheelchairsystem, the pressure sensor can detect when someone issitting in a wheelchair. After that, the pressure sensor
starts collecting each postural movement data. So, fromthat data, it is easily possible to recognize the users’ regular activities. This system cannot be able to detect
when someone is standing up from the wheelchair andfall immediately. Moreover, many established methods

a patient’s posture frequently alters as they attempt tostand up from a wheelchair. Their backrest pressure sensor and seat pressure sensor both experience a gradualreduction in pressure as they lean forward. They consequently slowly adopt a dangerous position. Our system
alerts the caregivers to take action since the patient could
unexpectedly stand up from the wheelchair or fall while
seated in a risky position.

are available for fall detection-related alarm systems innursing homes or hospitals. Nevertheless, when patientsalready stand up from their wheelchairs may already be
too late for caregivers to take necessary actions. Keeping
3情報処理学会 IoT 行動変容学研究グループ（SIG-BTI）

図3

座位動作の圧力センサデータ（単位：約ピコファラ

図5立ち上がり動作に挑戦する圧力センサデータ（単位：ド、pF）

約ピコファラド、pF）

Figure 3 Pressure sensor data for sitting activity (ap-

Figure 5

proximately in picofarad, pF)

tivity (approximately in picofarad, pF)

Pressure sensor data for trying to stand-up ac-

We obtained 16,000 columns of textile pressure sensor data for each activity in our experiments. Since thepressure sensor data is complicated, we did three experiments for better accuracy. We extracted mean, variance,
minimum, and maximum features from the raw pressure
sensor data for the ﬁrst experiment. Using a 1-second
window size, we set the features for 50% overlapping
data. We used one traditional machine learning model
K-Nearest Neighbor (KNN), for predicting the activity.
In the second experiment, we extracted ten statisticalfeatures, i.e., mean, median, standard deviation (STD),
図4

運動時の圧力センサデータ（単位：約ピコファラド、

minimum, maximum, median absolute deviation (MAD),pF）

interquartile range (IQR), Kurtosis, skewness, and vari-

Figure 4 Pressure sensor data for exercising activity

ance. Then for the third experiment, we again collected(approximately in picofarad, pF)

data with one person on a diﬀerent date. Moreover, weused the same subject data for training and testing, but

4.2. Experimental Setup

they were collected on diﬀerent dates.

We collected a sequence of three activity data suchas sitting, exercising, and trying to stand up using

5. Result Analysis

wheelchair pressure sensors. We analyze those data us-

In the ﬁrst experiment with four statistical features anding a machine learning algorithm to predict the activity

one person leave out the method, we achieved 84% accu-

in diﬀerent methodologies. The time duration for con-racy. For trying to stand up activity, we got 81% F1-

ducting each activity is 60 seconds. Data were collectedscore. Then we extracted six more statistical features,at a constant rate of 8Hz from e-textile pressure sensors.

and with them, our model achieved 86% accuracy and

For the ﬁrst experiment, in the laboratory environment,

83% F1-score for trying to stand up activity prediction.

we collected three subjects’ data, and we considered 2-

In the personalized experiment, we achieved 91% accuracy and 94% F1-score for trying to stand-up activity

person data for training and diﬀerent 1-person data for
testing. Figures 3-5 show the sum of each activity’s pressure data (approximately in picofarad, pF).

prediction. The result of our experiment with trying tostandup prediction demonstrates that adding more sta-4

情報処理学会 IoT 行動変容学研究グループ（SIG-BTI）

表1

マルチクラス分類の結果

Table 1

Multi-Class Classiﬁcation Results for three experiments, showing Macro average and weighted average for

precision (Prec.), recall (Rec.), and F1-Score (F1)

Activities

Experiment 1:
Leave-one-out

Experiment 2:
Leave-one-out

Experiment 3:
Personalized models

4 features

10 features

10 features

Prec.

Rec.

F1

Prec.

Rec.

F1

Prec.

Rec.

F1

Sitting

0.95

0.93

0.94

0.96

0.93

0.95

0.86

0.93

0.89

Exercise

0.87

0.68

0.76

0.86

0.75

0.80

0.90

0.93

0.91

Trying to Stand-up

0.73

0.90

0.81

0.78

0.90

0.83

1.00

0.88

0.94

Accuracy

0.84

0.86

0.91

Macro Average

0.85

0.84

0.84

0.87

0.86

0.86

0.92

0.91

0.91

Weighted Average

0.85

0.84

0.84

0.87

0.86

0.86

0.92

0.91

0.91

図6

4 つの特徴量の場合の混同行列

Prec.: Precision, Rec.: Recall, F1: F1-Scoretistical features has increased the accuracy of our model.
In particular, we observed that the accuracy of our model
improved from 84% to 86% when utilizing a one-person
leave-out method by increasing the number of statisticalfeatures from four to ten. Table one shows the detailprediction result. From experiment 1 and 2, we observedthat with one person leave out method, exercise data aremostly misclassiﬁed with trying to stand up activity data.
However, we achieved the highest accuracy with the personalized model because of less misclassiﬁcation between
exercise and trying to stand up data. Figure 6-8 shows
confusion matrix for each experiment, respectively.

6. Conclusion
The smart technology described in this paper uses etextile pressure sensors and machine learning algorithms

Figure 6

Confusion matrix with 4 statistical features

to detect attempts to stand up by wheelchair users. Thissystem’s deployment attempts to reduce the possibil-

In summary, the safety and well-being of older adults

ity of unintentional falls happening in healthcare facil-and dementia patients at risk of falling can be signiﬁ-

ities, including hospitals and nursing homes. The per-cantly improved using e-textile pressure sensors in fallsonalized model showed the highest level of accuracy at

prevention systems. These sensors can recognize move-

91% by combining a pressure matrix array conﬁguration

ment patterns and pressure variations, enabling earlywith the KNN algorithm. According to these ﬁndings,

identiﬁcation of falls and other abnormalities. These de-a wheelchair system with an e-textile pressure sensor isvices can assist in preventing falls and lowering the riska helpful tool for preventing unexpected falls. The sug-of injuries by providing real-time feedback and alarms togested technology is intended to anticipate attempts tocaregivers and healthcare professionals. E-textile pres-stand up from a wheelchair, which can also carry a sig-

sure sensors have the potential to be an essential factor

niﬁcant risk of injury.
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