Motion Sensor–Based Fall Prevention for Senior Care: A Hidden Markov Model with Generative Adversarial Network Approach
Published Online:14 Mar 2023https://doi.org/10.1287/isre.2023.1203
References
- (2018) MIS quarterly research curation on health information technology. Management Inform. Systems Quart. Res. Curations 1–14. Accessed December 15, 2022, https://www.misqresearchcurations.org/blog/2018/6/20/health-information-technology.Google Scholar
- (2019) Entropic GANs meet VAEs: A statistical approach to compute sample likelihoods in GANs. Internat. Conf. Machine Learn., 414–423.Google Scholar
- (2020) Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management. Management Inform. Systems Quart. 44(1):185–200.Google Scholar
- (2015) Predictive analytics for readmission of patients with congestive heart failure. Inform. Systems Res. 26(1):19–39.Link, Google Scholar
- (1998) A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Internat. Comput. Sci. Inst. 4(510):126.Google Scholar
- (2016) A fall detection method based on acceleration data and hidden Markov model. IEEE Internat. Conf. Signal Image Processing, 684–689.Google Scholar
- (2017) Analysis of public datasets for wearable fall detection systems. Sensors (Basel) 17(7):1513.Crossref, Google Scholar
- (2021) Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities. ACM Comput. Surveys 54(4):1–40.Google Scholar
- (2018) Engaging voluntary contributions in online communities: A hidden Markov model. Management Inform. Systems Quart. 42(1):83–100.Crossref, Google Scholar
- (2017) Generating multi-label discrete patient records using generative adversarial networks. Proc. Machine Learning Healthcare, vol. 68, 1–20.Google Scholar
- (2022) Cross-lingual cybersecurity analytics in the international dark web with adversarial deep representation learning. Management Inform. Systems Quart. 46(2):1209–1226.Crossref, Google Scholar
- (2012) Semi-supervised fall detection algorithm using fall indicators in smartphone. Proc. Sixth Internat. Conf. Ubiquitous Inform. Management Comm. (ACM, New York), 1–9.Google Scholar
- (2018) Medical costs of fatal and nonfatal falls in older adults. J. Amer. Geriatric Soc. 66(4):693–698.Crossref, Google Scholar
- (2015) Proposal and Experimental Evaluation of Fall Detection Solution Based on Wearable and Depth Data Fusion, Advances in Intelligent Systems and Computing, vol. 399 (Springer-Verlag, Berlin).Google Scholar
- (2020) Automatic detection of human’s falls from heights for airbag deployment via inertial measurements. Automic Construction 120:103358.Crossref, Google Scholar
- (2016) NIPS 2016 tutorial: Generative adversarial networks. 30th Conf. Neural Inform. Processing Systems (NIPS, La Jolla, CA).Google Scholar
- (2020) The future of technology and marketing: A multidisciplinary perspective. J. Acad. Mark. Sci. 48:1–8.Google Scholar
- (2018) Traits of successful research contributions for publication in ISR: Some thoughts for authors and reviewers. Inform. Systems Res. 29(4):779–786.Link, Google Scholar
- (2004) Design science in information systems research. Management Inform. Systems Quart. 28(1):75–105.Crossref, Google Scholar
- (2017) Model selection for Gaussian mixture models. Statist. Sinica 27(1):147–169.Google Scholar
- (2020) Association between chronic diseases and falls among a sample of older people in Finland. BMC Geriatrics 20(1):1–12.Crossref, Google Scholar
- (2021) Risk factors for recurrent falls in older adults: A systematic review with meta-analysis. Maturitas 144:23–28.Crossref, Google Scholar
- (2016) Composing graphical models with neural networks for structured representations and fast inference. 30th Conf. Neural Inform. Processing Systems (NIPS, La Jolla, CA).Google Scholar
- (2004) Agility and balance differ between older community and retirement facility residents. J. Appl. Gerontology 23(4):457–468.Crossref, Google Scholar
- (2012) Evaluation of fall detection classification approaches. Fourth Internat. Conf. Intelligent Adv. Systems (IEEE, Piscataway, NJ), vol. 1, 131–136.Google Scholar
- (2014) X-factor HMMs for detecting falls in the absence of fall-specific training data. Pecchia L, Chen LL, Nugent C, Bravo J, eds. Ambient Assisted Living and Daily Activities, Lecture Notes in Computer Science, vol 8868 (Springer, Cham, Switzerland), 1–9.Crossref, Google Scholar
- (2017) Efficacy of a health app for obesity and overweight management: A hidden Markov model. Proc. Internat. Conf. Inform. Systems (Association for Information Systems, Atlanta).Google Scholar
- (2015) Deep learning. Nature 521(7553):436–444.Crossref, Google Scholar
- (2017) Personal and social usage: The origins of active customers and ways to keep them engaged. Management Sci. 64(6):2473–2495.Link, Google Scholar
- (2014) Digression and value concatenation to enable privacy-preserving regression. Management Inform. Systems Quart. 38(3):679–698.Crossref, Google Scholar
- Liang S, Chu T, Lin D, Ning Y, Li H, Zhao G (2018) Pre-impact alarm system for fall detection using MEMS sensors and HMM-based SVM classifier. Proc. 2018 40th Ann. Internat. Conf. IEEE Eng. Med. Biol. Soc. (IEEE, Piscataway, NJ), 4401–4405.Google Scholar
- (2014) Fall-detection algorithm using 3-axis acceleration: Combination with simple threshold and hidden Markov model. J. Appl. Math. 2014:1–8.Crossref, Google Scholar
- (2017) Healthcare predictive analytics for risk profiling in chronic care: A Bayesian multitask learning approach. Management Inform. Systems Quart. 41(2):473–496.Crossref, Google Scholar
- (1995) Mixture models: Theory, geometry and applications. NSF-CBMS Regional Conf. Ser. Probab. Stat., 163.Google Scholar
- (2019) Detection of human fall using floor vibration and multi-features semi-supervised SVM. Sensors (Basel) 19(17):3720.Crossref, Google Scholar
- (2018) Semi-supervised learning with generative adversarial networks for chest x-ray classification with ability of data domain adaptation. Proc. IEEE Internat. Sympos. Biomedical Imaging (IEEE, Piscataway, NJ), 1038–1042.Crossref, Google Scholar
- (2019) Image super-resolution using progressive generative adversarial networks for medical image analysis. Computerized Medical Imaging Graphics 71:30–39.Crossref, Google Scholar
- (2013) Gaussian mixture based HMM for human daily activity recognition using 3D skeleton features. Eighth IEEE Conf. Indust. Electronic Appl. (IEEE, Piscataway, NJ), 567–572.Google Scholar
- (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2):257–286.Crossref, Google Scholar
- (2017) Editor’s comments: Avoiding type III errors: Formulating is research problems that matter. Management Inform. Systems Quart. 41(2):3–7.Google Scholar
- (2019) A patient-specific single sensor IoT-based wearable fall prediction and detection system. IEEE Trans. Neural Systems Rehabilitation Engrg. 27(5):995–1003.Crossref, Google Scholar
- (2008) Characteristics of hospital inpatient falls across clinical departments. Gerontology 54(6):342–348.Crossref, Google Scholar
- (2017) SisFall: A fall and movement data set. Sensors (Basel) 17(1):198.Crossref, Google Scholar
- (2018) Real-life/real-time elderly fall detection with a triaxial accelerometer. Sensors (Basel) 18(4):1–18.Crossref, Google Scholar
- (2019) Semi-supervised learning of hidden Markov models for biological sequence analysis. Bioinformatics 35(13):2208–2215.Crossref, Google Scholar
- U.S. Census Bureau (2020) American community survey: Age and sex. Accessed December 15, 2022, https://data.census.gov/cedsci/table?q=PEPAGE&t=Age%20and%20Sex&tid=ACSST1Y2019.S0101&hidePreview=false.Google Scholar
- (2021) Research of HMM-based fall detection system for elderly. J. Comput. 32(1):27–38.Google Scholar
- (2019) Ea-GANs: Edge-aware generative adversarial networks for cross-modality MR image synthesis. IEEE Trans. Medical Imaging 38(7):1750–1762.Google Scholar
- (2017) Hidden Markov model-based fall detection with motion sensor orientation calibration: A case for real-life home monitoring. IEEE J. Biomedical Health Inform. 22(6):1847–1853.Crossref, Google Scholar
- (2022) Wearable sensor-based chronic condition severity assessment: An adversarial attention-based deep multisource multitask learning approach. Management Inform. Systems Quart. 46(3):1355–1394.Crossref, Google Scholar
- (2021) Fall detection with wearable sensors: A hierarchical attention-based convolutional neural network approach. J. Management Inform. Systems 38(4):1095–1121.Crossref, Google Scholar
- (2016) Modeling idle customers to tackle the sparsity problem in time-dependent recommendation. Proc. Internat. Conf. Inform. Systems (Association for Information Systems, Atlanta).Google Scholar
- (2021) A deep learning approach for recognizing activity of daily living (ADL) for senior care: Exploiting interaction dependency and temporal patterns. Management Inform. Systems Quart. 45(2):859–896.Crossref, Google Scholar
- (2020) Human identification for activities of daily living: A deep transfer learning approach. J. Management Inform. Systems 37(2):457–483.Crossref, Google Scholar

