1. Introduction
The construction industry often obliges workers to operate in poor working conditions, in which serious accidents occur due to high labor intensity, long working hours, and dangerous working environments [
1]. It is also inherently a dangerous work environment and potentially contributes to workplace injuries. The Bureau of Labor Statistics (BLS, USA) cited six representative accidents that can occur on construction sites: (1) fires and explosion, (2) exposure to harmful substances or environments, (3) contact with objects and equipment, (4) violence and other injuries by persons or animals, (5) falls, slips, trips, and (6) transportation incidents. Among them, 887 of all construction workers were fatally injured due to falls, slips, and trips, which accounted for 17.23% of all fatal industrial injuries [
2]. They also reported that 5147 people suffered fatal work injuries and fall accidents in construction sites, accounting for 39.2% of the fatalities in the sector in 2018. The Centers for Disease Control & Prevention (South Korea) reported that 14,306 people were injured due to falls in all industries, of which 8607 were construction workers, comprising 37.56% of the total for 2018. Falls on construction sites can cause bone fractures and bruises, and it is especially important to assess the victim as soon as possible when a fall occurs. This is because transportation to a medical institution can help prevent further injuries. Although many studies have been conducted to develop sensors and systems to monitor fall accidents on construction sites [
3], only a few studies have attempted to monitor and analyze falls occurring on stairs.
Stair falls are the leading cause of job-related injuries and even workers’ deaths on construction sites [
4]. Falls often occur when a heavy load is placed on the back, balance is lost, the rails are slippery or there is no stair rail, or simply due to stumbles when descending [
5,
6,
7]. The Occupational Safety & Health Research Institute (OSHRI) in Korea reported that a total of 16,231 people were injured due to falls on construction sites, and that 18% of workers experienced falls on the stairs [
8]. In the case of construction sites, there is no closed-circuit television (CCTV) and no way to check the movement of workers, unlike general work spaces. As a result, identifying a fall from the stairs is more difficult than in other places on the construction site. Therefore, fall detection systems are needed to monitor and analyze falls that can occur on the stairs.
Many studies on the development of fall detection systems have been conducted to distinguish falls from general activities of daily life, and have mainly adopted a technique of analyzing body movement using accelerometer sensors [
9,
10]. However, insufficient attention has been given to applying this idea to a fall detection system specifically for construction site workers. Yang et al. (2016) developed fall detection system using a three-axis accelerometer sensor, and they conducted experiments under two conditions (normal walk, near-miss fall) to detect near-miss falls on a construction site [
11]. However, because the three-axis accelerometer sensor is attached to the waist, it can impede the worker’s movement, and does not take into account changes in weight when carrying heavy loads on the shoulders. Dzeng et al. (2014) developed a fall detection system using a three-axis accelerometer sensor in a smart phone. They conducted a fall test on a static posture (standing, squatting) [
12]. However, carrying a smartphone on a construction site can be inconvenient, and this approach has limitations in terms of measuring body movement. The three-axis acceleration sensor is useful for detecting falls, but it may yield false information. Due to the noise caused by movement, the sensor data may be unable to distinguish between falls and ordinary movement.
On construction sites, workers are frequently exposed to fall risks and thus it is important to detect foot areas and detect changes in the balance of both feet [
13]. Gifford (2007) and Altman et al. (2017) reported that potential sources of falls are bad interactions between the foot and floor-surface, with changes of workers’ gait patterns on construction sites [
14,
15]. Light et al. (2015) and Chaccour et al. (2016) developed an insole-type fall detection system using a pressure sensor and conducted experiments to detect falls [
16,
17]. They proposed a system for evaluating falls by analyzing changes in pressure on the sole surface. However, the FSR sensor used by them has low durability. In particular, low durability can be a problem in detecting worker falls, because there are more cases of carrying heavy loads on construction sites than in everyday life, and it may not be suitable for detecting falls due to sensor damage or distortion caused by external impact.
In general, all construction workers must wear a safety helmet and safety shoes [
18]. Research conducted by Melzner (2012) reported that wearing appropriate safety equipment contributed to a reduction of fall accidents of more than 30% [
19]. However, existing safety helmets and shoes are used only to protect construction workers against external shocks. In previous studies, safety helmets and shoes with IMU sensors were developed [
20,
21,
22,
23].
The purpose of our research was to evaluate fall incidence by simulating three experimental conditions using a three-axis accelerometer sensor and pressure sensor. In our previous studies, we developed a smart helmet and textile-based smart insole sensor [
21,
24,
25]. Based on the data collected from the developed sensors, we analyzed whether our proposed sensor could distinguish between falls and non-falls.
4. Discussion
In this study, we developed Smart Helmet and Smart Shoes to detect near fall incidence during stair descent and analyzed the changes in weight bearing.
Due to wearing a backpack weighing 15 kg (E2), the plantar pressure value on both the right and left foot was higher than that of natural descent (E1). As a result, the subject’s center of weight and neck were tilted forward to avoid falling. However, this can put a strain on back and cause a slipped disc [
30]. Johnson et al. (1995) reported that backpack weight can cause back pain, muscle soreness, numbness, shoulder pain, and muscle fatigue [
31]. For this, a previous study has been undertaken on safe weight carrying limits for construction workers [
32]; the weight limit per person was 15 kg on construction site in Korea. Therefore, we applied a weight of 15 kg.
In the near fall simulation (E3), the plantar pressure value in each side of the foot was higher in general compared to that of E1 and E2. The difference in plantar pressure between the right and left foot in E3 was also greater than that of E1 and E2. This is considered to be caused by the near fall incident in E3. Antwi-Afari et al. (2018) undertook a fall risk assessment of construction workers using center of pressure (COP) with wearable insole pressure sensors. They demonstrated that the difference of peak pressure with the loss of balance events was about four times higher than during natural stair descent (
p-value < 0.05) [
33]. We also confirmed similar findings in this study.
In the case of E1 and E2, we found an interesting point where the left foot had a higher plantar pressure value than the right foot. It appeared that the size of the sensor was the same, i.e., 2 × 2 cm, but the capacitance value of one channel may have been larger due to human error. This may also have been due to differences in footwear and foot structure. Moreover, the reason for the higher left foot pressure in the E1 and E2 experiments was because the handrail was located on the left side of the stairs where the experiment was performed, while the wall was on the right side. Hence, the subjects moved their weight toward the handrail to avoid falling unintentionally during the experiment. The results of this study are expected to help prevent the occurrence of back injury by monitoring the individual load assessment of construction workers. It also shows that Smart Shoes can check for near fall incidents.
Rao et al. (2012) studied the effect of activity on regional plantar loading in different regions of the foot during stair descent [
29]. They observed high plantar pressure values in the heel and central forefoot regions, which is in perfect agreement with the results of our experiment. They also reported that it is controversial to report which region has the highest pressure. In the studies by Rozema et al. (1996) and Maluf et al. (2004), lower plantar pressure values in the heel were reported during stair descent [
34,
35]. On the other hand, some studies have observed lower pressure values at the hallux [
36].
Looking at the plantar pressure distribution of both feet in the mediolateral area, we found that the pressure values of lateral areas was higher than those of medial areas for both feet in all experiments, as shown in
Table 4 and
Figure 13. In our experiment, we selected subjects who had never had walking-related musculoskeletal disorders, so the pressure distribution in the lateral area was larger to prevent from falling. Conversely, larger pressure distribution in the medial area can be seen among knock-knee patients.
SVM data is widely used to recognize activity in a way that obtains magnitude by ignoring the direction of the vector about three axes [
37]. The average
SVM difference between E1 and E2 was 0.081 g for the right foot and 0.025 g for the left foot, and the average difference between
SVM between E1 and E3 was 0.212 g for the right foot and 0.804 g for the left foot. E3 has a larger
SVM average difference than E1 compared to E2, and has the same results as the previous study [
38]. In E3, as soon as the right foot touched the ground on the stairs, the body was out of balance and sharply tilted to the right, resulting in high
SVM data on the right foot. As a result, the
SVM difference between the right and left foot in E3 was higher than those of E1 and E2, which means that the balance of the body is unstable.
Previous studies related to fall detection systems using textile pressure sensors and three-axis accelerometers have shown that there are few studies that detect falls using both sensors simultaneously [
39]. They reported that the detection of falls using only pressure sensors is poorly-suited to determining if the subject is falling or if the body merely moved slightly left and right while descending the stairs. They also reported that three-axis accelerometer sensor data has limitations for identifying falls, because the three-axis accelerometer data from normal, everyday movements and those from falls are similar, and disturbances cause noise in the data. They found that the best fall detection rates were obtained by analyzing the data of the pressure sensor and the accelerometer sensor together.
Bai et al. (2013) reported that if the body flips after the fall, it will face in a different direction, and a change will occur in the
x,
y, and
z axes before and after the fall [
40]. Based on this, we selected the
x-axis to observe changes of the left and right tilt for correlation analyses with the pressure sensor.
A correlation analysis was performed to detect falls using raw data of the whole plantar pressure and raw data of the
x-axis in E3; the R
2 values of the right foot showed negative correlation among all subjects (
p-value <0.05) as shown in
Table 6. When the value of the textile pressure sensor of the right foot was the largest, it was the moment when the center of the body moves to the right. Therefore, the
x-axis value of the accelerometer sensor was lowered and then the center of the body returned to its original state to maintain of body balance. At the same time, the value of the textile pressure sensor decreased and the
x-axis value increased. So, the data of the two sensors showed a negative correlation.
We have confirmed that our sensors can distinguish between a worker lifting and not lifting heavy loads, and the difference between falling and non-falling. Our system is also very inexpensive and has the advantage of being applicable to construction sites.
Nevertheless, there are a few weaknesses in our research. We considered and discussed only the results of our proposed experimental procedure without analyzing the similarity and relationship between fall cases which actually occur on construction sites and those in our experiments. The number of subjects participating in the experiment was limited and we did not test subjects of different ages, so it is difficult to generalize the results of this study. We did not consider the height of the stairs, the degree of slippage, and the ambient light in these experiments. In addition, although it would need to be possible to use such technology for a long time in a real construction site setting, this study did not evaluate the performance verification of the battery. According to previous studies [
41,
42], the pressure data for each foot area differed with changes in walking speed during stair descent. However, we did not consider the speed of the descent.
In the future, we intend to design an experimental protocol by subdividing and modeling the fall types that occur on the stairs of an actual construction site. Based on this, we also intend to prove the reliability of our experimental procedure and plan to recruit more subjects to generalize the research results in consideration of the height, slipperiness, and illumination of the stairs, and walking speeds of the subjects.