Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 Threshold-based preimpact fall detection algorithm was developed to utilize wearable airbag that minimize the impact of falls on the user's body. + 1353 word(s) 1353 2020-03-06 02:40:03 |
2 format correct -1 word(s) 1352 2020-03-13 06:52:40 | |
3 format correct + 3 word(s) 1355 2020-11-01 09:52:12 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Jung, H.; Koo, B.; Kim, J.; Kim, T.; Nam, Y.; Kim, Y. Wearable Airbags. Encyclopedia. Available online: https://encyclopedia.pub/entry/374 (accessed on 28 March 2024).
Jung H, Koo B, Kim J, Kim T, Nam Y, Kim Y. Wearable Airbags. Encyclopedia. Available at: https://encyclopedia.pub/entry/374. Accessed March 28, 2024.
Jung, Haneul, Bummo Koo, Jongman Kim, Taehee Kim, Yejin Nam, Youngho Kim. "Wearable Airbags" Encyclopedia, https://encyclopedia.pub/entry/374 (accessed March 28, 2024).
Jung, H., Koo, B., Kim, J., Kim, T., Nam, Y., & Kim, Y. (2020, March 11). Wearable Airbags. In Encyclopedia. https://encyclopedia.pub/entry/374
Jung, Haneul, et al. "Wearable Airbags." Encyclopedia. Web. 11 March, 2020.
Wearable Airbags
Edit

Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously.

falls airbag threshold-based IMU complementary filter

1. Introduction

With the world’s population ageing, there is an increasing focus on age-related health issues. Fall-related injuries are the primary cause of injury and mortality in the elderly. According to the WHO, every year, 37.3 million people are injured severely enough to receive medical treatment, and 646,000 people die due to falls. Adults over 65 years of age suffer the greatest number of fatal falls [[1]]. Reduced levels of bone density, decreased muscle mass, as well as slow reflexes increase the risk of fall-related injuries in the elderly [[2][3]]. Hip fractures are considered the most dangerous among various injuries, as they can reduce mobility and lead to many complications, and even death [[4]]. In addition, there is a social dimension to this issue, because it mandates special care for the elderly. From an economic perspective, fall-related medical expenses in the elderly were reported to have accounted for as much as USD 50 billion for nonfatal falls and 754 million for fatal falls in the year 2015 alone [[5]]. Therefore, falls and fall-related injuries are major sources of physical, social, and economic problems among the elderly.

In the past, many studies have tried to improve the physical performance of the elderly by implementing exercise programs to help prevent falls. Lord et al. [[6]] conducted a fall prevention exercise program based on physiological profile assessments and sit-to-stand tests. They found that while the program improved knee flexion and visual ability, there was no significant reduction in fall risk. Røyset et al. [[7]] also conducted a fall prevention program using the Norwegian version of the fall risk assessment method, “STRATIRY” (score 0–5), but achieved no significant improvement statistically when compared to the control group.

2. Enhanced Algorithm for Detection of Preimpact Fall for Wearable Airbags

There are many commercially available hip protection pads that help mitigate the impact of a fall, but they are inconvenient to use in daily life as they must be worn under the clothes. Additionally, their performance varies, as they tend to slide away from the correct position when worn over time [[8][9]]. To ensure better impact attenuation during falls, some researchers have also developed wearable airbags [[10][11][12]]. Jeong et al. [[13]] developed a hip impact simulator and compared the impact attenuation performance of hip protection pads with a wearable airbag. Their results showed that the wearable airbag did a better job of cushioning the impact from a fall because of its deformation at the time of impact, which increased the contact time and area involved during the impact. However, to successfully utilize a wearable airbag, a fall detection algorithm with a high level of accuracy and sufficient lead time to inflate the airbag before the impact must be used.

Falls can be detected either through stationary devices, such as cameras, or wearable sensors. Rougier et al. [[14]] used a particle filter to track human head position in three dimensions with a single camera, and detected falls using the vertical displacement and velocity of the head. Bian et al. [[15]] tracked the head and waist using a depth camera to detect falls. They applied a support vector machine classifier and achieved 95.8% sensitivity, 100% specificity, and 97.9% accuracy. Wang et al. [[16]] created a surveillance video–based fall detection system using PCANet and a linear support vector machine classifier that achieved 88.87% sensitivity and 98.9% specificity. Li et al. [[17]] used Kinect sensors to distinguish falls based on whether the zero moment point was within the dynamic supporting area or not; they obtained 100% sensitivity, 81.3% specificity, and 91.7% accuracy. However, detecting falls using such stationary camera devices has some drawbacks, including limited coverage area and privacy issues.

In contrast, many fall detection systems have been developed that use wearable sensors, such as inertial measurement unit (IMU) and electromyography (EMG) sensors, based on machine learning (ML) and threshold-based approaches. Rescio et al. [[18]] attached an EMG sensor to the soleus muscle to detect falls and selected the optimal feature among ten types of time-domain features using a Markov random field-based Fisher-Markov selector. They achieved 89.5% sensitivity and 91.9% specificity. Yoo et al. [[19]] obtained acceleration properties from an IMU positioned on the wrist that detects falls using an artificial neural network, and achieved 100% accuracy. Aziz et al. [[20]] used 3-axis acceleration data from an IMU attached on the waist, and compared its performance between logistic regression, decision tree, Naïve Bayes, K-nearest neighbor, and support vector machine. Among them, the support vector machine showed the best performance, with 96% sensitivity and 96% specificity. In general, ML-based fall detection algorithms show good performance, but it takes considerable time to extract features and train classifiers. Therefore, ML-based algorithms are not appropriate for preimpact fall detection where a rapid detection time is required. Therefore, threshold-based fall detection algorithms would be more suitable for predicting falls because of their low computational requirements and quick response time.

Thanh et al. [[10]] captured 3-axis acceleration data from the waist position, to which they applied a Kalman filter to reduce measurement and sensor errors. Their threshold-based fall detection algorithm used the root mean square of acceleration, vertical angle, and vertical velocity, and achieved 100% fall detection accuracy. Zhong et al. [[11]] proposed a threshold-based fall detection algorithm using vertical displacement and vertical velocity from an IMU sensor on the front waist. They obtained 93.6% sensitivity and 95.6% specificity with an average lead time of 363 ms. Nyan et al. [[21]] developed a preimpact fall detection algorithm that used angles calculated from the torso and thigh (with the help of 2 IMUs), as well as the correlation coefficients of the torso and thigh angles. The algorithm achieved 100% specificity, 95.2% specificity, and a lead time of 700 ms. Wang et al. [[22]] calculated acceleration magnitude, acceleration cubic-product-root magnitude, and angular velocity cubic-product-root magnitude from the acceleration and angular velocity data obtained from an IMU placed on the front chest to develop a threshold-based fall detection algorithm. In the study, the algorithm was evaluated using their experimental data and two different public datasets (Cogent Labs and UMAFall). They achieved 98.9%, 98.0%, and 96.6% sensitivity and 100%, 96.6%, and 83.2% specificity based on their experimental data, cogent labs dataset, and UMAFall dataset, respectively. Ahn et al. [[23]] developed a threshold-based fall detection algorithm based on acceleration SVM, angular velocity SVM, and triangle feature calculated from acceleration and angular velocity data from a sensor placed on the front waist. They reported 100% sensitivity and 83.9% specificity using the SisFall public dataset. While some of the previously discussed threshold-based fall detection algorithms showed high detection accuracies, they would not be practical in real-life applications because only less-dynamic (walking, standing, sitting, etc.) or typical motions (climbing upstairs/downstairs, etc.) were included in these studies.

In this study, 6 types of simulated falls and 14 activities of daily life (ADLs), including some highly dynamic motions, were selected to simulate real-life situations. To capture inertial data, an IMU was used. From the captured inertial data, acceleration SVM, angular velocity SVM, and vertical angle were calculated using complementary filter with a proportional integral (PI) controller. Subsequently, a threshold-based preimpact fall detection algorithm was developed based on these parameters. The algorithm’s performance was evaluated in a lab experiment involving 30 research participants. For an objective evaluation, we evaluated the algorithm using the SisFall public dataset as well [[24]].

References

  1. World Health Organization (WHO). WHO Global Report on Falls Prevention in Older Age 2007. Available online: https://extranet.who.int/agefriendlyworld/wp-content/uploads/2014/06/WHo-Global-report-on-falls-prevention-in-older-age.pdf (accessed on 30 December 2019).
  2. Sabine Verschueren; An Bogaerts; Christophe Delecluse; Albrecht L Claessens; Patrick Haentjens; Dirk Vanderschueren; Steven Boonen; The effects of whole-body vibration training and vitamin D supplementation on muscle strength, muscle mass, and bone density in institutionalized elderly women: A 6-month randomized, controlled trial. Journal of Bone and Mineral Research 2010, 26, 42-49, 10.1002/jbmr.181.
  3. Aida Softic; Azijada Beganlić; Nurka Pranjić; S Sulejmanovic; The Influence of the Use of Benzodiazepines in the Frequency Falls in the Elderly. Medical Archives 2013, 67, 259, 10.5455/medarh.2013.67.256-259.
  4. Gunhild Hagen; Jon Magnussen; Grethe Tell; Tone Omsland; Estimating the future burden of hip fractures in Norway. A NOREPOS study. Bone 2020, 131, 115156, 10.1016/j.bone.2019.115156.
  5. Curtis S. Florence; Gwen Bergen; Adam Atherly; Elizabeth Burns; Judy Stevens; Cynthia Drake; Medical Costs of Fatal and Nonfatal Falls in Older Adults. Journal of the American Geriatrics Society 2018, 66, 693-698, 10.1111/jgs.15304.
  6. Stephen R. Lord; Anne Tiedemann; Kirsten Chapman; Bridget Munro; Susan M. Murray; M Gerontology; Gymnast Recreat Ther; Catherine Sherrington; The Effect of an Individualized Fall Prevention Program on Fall Risk and Falls in Older People: A Randomized, Controlled Trial. Journal of the American Geriatrics Society 2005, 53, 1296-1304, 10.1111/j.1532-5415.2005.53425.x.
  7. Bodil Røyset; Bente Talseth-Palmer; Stian Lydersen; Per G. Farup; Effects of a fall prevention program in elderly: a pragmatic observational study in two orthopedic departments.. Clinical Interventions in Aging 2019, 14, 145-154, 10.2147/CIA.S191832.
  8. M.F. Swiontkowski; Effectiveness of hip protectors for preventing hip fractures in elderly people: systematic review. Yearbook of Orthopedics 2007, 2007, 82-83, 10.1016/s0276-1092(08)70085-3.
  9. S. Derler; A.B. Spierings; K.-U. Schmitt; Anatomical hip model for the mechanical testing of hip protectors. Medical Engineering & Physics 2005, 27, 475-485, 10.1016/j.medengphy.2005.02.001.
  10. Pham Van Thanh; Tan T. D -; Dinh-Chinh Nguyen; Nguyen Duc Anh; Dang Nhu Dinh; S. El-Rabaie; Kumbesan Sandrasegaran; Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers. Arabian Journal for Science and Engineering 2018, 44, 3329-3342, 10.1007/s13369-018-3496-4.
  11. Zhichao Zhong; Feiyu Chen; Qian Zhai; Zhiqiang Fu; João P. Ferreira; Yanjie Liu; Jingang Yi; Tao Liu; A Real-time Pre-impact Fall Detection and Protection System. 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) 2018, null, 1039-1044, 10.1109/aim.2018.8452687.
  12. Soonjae Ahn; Dagyeong Choi; Jongman Kim; Seongjung Kim; Youngjae Jeong; Min Jo; Youngho Kim; Optimization of a Pre-impact Fall Detection Algorithm and Development of Hip Protection Airbag System. Sensors and Materials 2018, 30, 1743, 10.18494/sam.2018.1876.
  13. Youngjae Jeong; Soonjae Ahn; Jongman Kim; Seongjung Kim; Bummo Koo; Jeseong Ryu; Youngho Kim; Impact Attenuation of the Soft Pads and the Wearable Airbag for the Hip Protection in the Elderly. International Journal of Precision Engineering and Manufacturing 2019, 20, 273-283, 10.1007/s12541-019-00053-9.
  14. Caroline Rougier; Jean Meunier; Alain St-Arnaud; Jacqueline Rousseau; 3D head tracking for fall detection using a single calibrated camera. Image and Vision Computing 2013, 31, 246-254, 10.1016/j.imavis.2012.11.003.
  15. Zhen-Peng Bian; Junhui Hou; Lap-Pui Chau; Nadia Magnenat-Thalmann; Fall Detection Based on Body Part Tracking Using a Depth Camera. IEEE Journal of Biomedical and Health Informatics 2014, 19, 430-439, 10.1109/JBHI.2014.2319372.
  16. Shengke Wang; Long Chen; Zixi Zhou; Xin Sun; Junyu Dong; Human fall detection in surveillance video based on PCANet. Multimedia Tools and Applications 2015, 75, 11603-11613, 10.1007/s11042-015-2698-y.
  17. Min Li; Guanghua Xu; Bo He; Xiaolong Ma; Jun Xie; Pre-Impact Fall Detection Based on a Modified Zero Moment Point Criterion Using Data From Kinect Sensors. IEEE Sensors Journal 2018, 18, 5522-5531, 10.1109/jsen.2018.2833451.
  18. Gabriele Rescio; Alessandro Leone; Pietro Siciliano; Supervised machine learning scheme for electromyography-based pre-fall detection system. Expert Systems with Applications 2018, 100, 95-105, 10.1016/j.eswa.2018.01.047.
  19. Sungil Yoo; Dongik Oh; An artificial neural network–based fall detection. International Journal of Engineering Business Management 2018, 10, 1-7, 10.1177/1847979018787905.
  20. Omar Aziz; Magnus Musngi; Edward J. Park; Greg Mori; Sn Robinovitch; A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Medical & Biological Engineering & Computing 2016, 55, 45-55, 10.1007/s11517-016-1504-y.
  21. M.N. Nyan; Francis E.H. Tay; E. Murugasu; A wearable system for pre-impact fall detection. Journal of Biomechanics 2008, 41, 3475-3481, 10.1016/j.jbiomech.2008.08.009.
  22. Fu-Tai Wang; Hsiao-Lung Chan; Ming-Hung Hsu; Cheng-Kuan Lin; Pei-Kuang Chao; Ya-Ju Chang; Threshold-based fall detection using a hybrid of tri-axial accelerometer and gyroscope.. Physiological Measurement 2018, 39, 10, 10.1088/1361-6579/aae0eb.
  23. Soonjae Ahn; Jongman Kim; Bummo Koo; Youngho Kim; Evaluation of Inertial Sensor-Based Pre-Impact Fall Detection Algorithms Using Public Dataset. Sensors 2019, 19, 774, 10.3390/s19040774.
  24. Angela Sucerquia; Jd López; Jesús Francisco Vargas-Bonilla; SisFall: A Fall and Movement Dataset. Sensors 2017, 19, 198, 10.3390/s17010198.
More
Information
Subjects: Physics, Applied
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , , , ,
View Times: 985
Revisions: 3 times (View History)
Update Date: 01 Nov 2020
1000/1000