Honghao Gao
Abstract
With the development of the Internet of Medical Things (IoMT), indoor wireless sensor networks (WSNs) have been used to monitor Alzheimer's disease patients daily and guide their behaviors. Alzheimer's disease may seriously impact patients’ memory, and thoughts of “what should I do” can unexpectedly form in their mind. This cognitive impairment can affect patients’ independence and well-being. As a basic infrastructure for future healthcare systems, WSN can collect patient behaviors, such as their positions and states, to support safety and health analyses. Therefore, this paper proposes a probabilistic model checking-based method to predict patient behaviors and detect abnormal behaviors related to mild cognitive impairment to help patients rebuild their confidence and perception. First, the layout of the home environment is abstracted as a formal grid, and a user activity model (UAM) is proposed in the form of discrete-time Markov chain (DTMC) to describe patients’ activity based on data collected by sensors. Second, because Alzheimer's patients with mild cognitive impairment (A-MCI) often forget their next daily activities, we classify and describe their daily behaviors as verification requirements in the form of probabilistic computational tree logic (PCTL). Then, the UAM is input into a probabilistic model checking tool and compared against the verification property PCTL to calculate the probability values and assess temporal behaviors. As result, the activity with the largest probability is selected for behavior guidance. Third, we demonstrate the process of detecting abnormalities, including activities with abnormal temporal behaviors and activities with normal temporal behaviors but unexpected probabilities that may be repeated more than twice. The key states are extracted from the UAM to specify the verification properties for abnormality detection. Finally, a case study is presented to demonstrate the usability and feasibility of our proposed method.
- [1] 2020. Dementia in China: Epidemiology, clinical management, and research advances [J]. The Lancet Neurology 19, 1 (2020), 81–92.Google Scholar
Cross Ref
- [2] Gauthier Serge, et al. 2006. Mild cognitive impairment [J]. The Lancet 367, 9518 (2006), 1262--1270.Google Scholar
- [3] 2018. A treatment-response index from wearable sensors for quantifying Parkinson's disease motor states [J].IEEE Journal of Biomedical and Health Informatics 22, 5 (2018), 1341–1349.Google ScholarCross Ref
- [4] 2018. Smart home-based prediction of multidomain symptoms related to Alzheimer's disease [J].IEEE Journal of Biomedical and Health Informatics 22, 6 (2018), 1720–1731.Google ScholarCross Ref
- [5] 2020. Modeling the behavior of persons with mild cognitive impairment or Alzheimer's for intelligent environment simulation [J]. User Modeling and User-Adapted Interaction 30, 5 (2020), 895–947.Google ScholarDigital Library
- [6] 2019. Remote health monitoring of elderly through wearable sensors [J]. Multimedia Tools and Applications 78, 17 (2019), 24681–24706.Google ScholarDigital Library
- [7] Shadab Ali Shaikh, Obaid Kazi, Mohd Adnan Ansari, et al. 2020. Medication adherence monitoring with tracking automation and emergency assistance [C]. International Conference on Mobile Computing and Sustainable Informatics. 523–532.Google Scholar
- [8] 2008. Biomarkers for early detection of Alzheimer pathology [J]. Neurosignals 16, 1 (2008), 11–8.Google ScholarCross Ref
- [9] 2016. Failure and abnormal behaviour detection in wireless sensor networks [C]. The 15th RoEduNet Conference: Networking in Education and Research. 1–5.Google Scholar
- [10] . 2003. Towards robotic assistants in nursing homes: Challenges and results [J].Robotics and Autonomous Systems 42, 3–4 (2003), 271–281.Google ScholarCross Ref
- [11] 2019. A survey on anomalous behavior detection for elderly care using dense-sensing networks [J]. IEEE Communications Surveys & Tutorials 22, 1 (2019), 352–370.Google ScholarDigital Library
- [12] 2021. IoT-based telemedicine for disease prevention and health promotion: State-of-the-art [J]. Journal of Network and Computer Applications 173, 102873 (2021), 1084–8045.Google ScholarDigital Library
- [13] . 2018. Early anomaly detection in smart home: A causal association rule-based approach [J]. Artificial Intelligence in Medicine 91 (2018), 57–71.Google ScholarCross Ref
- [14] 2010. A mobile multimedia technology to aid those with Alzheimer's disease [J]. IEEE MultiMedia 17, 2 (2010), 42–51.Google ScholarDigital Library
- [15] 2018. An assistive technology system that provides personalized dressing support for people living with dementia: Capability study [J]. JMIR Medical Informatics 6, 2 (2018), 21.Google ScholarCross Ref
- [16] . 2019. A study on a fall detection monitoring system for falling elderly using open source hardware [J]. Multimedia Tools and Applications 78 (2019), 28423–28434.Google ScholarDigital Library
- [17] 2019. Probabilistic analysis of temporal and sequential aspects of activities of daily living for abnormal behaviour detection [C]. IEEE SmartWorld. 723–730.Google Scholar
- [18] . 2017. Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing [J]. Multimedia Tools and Applications 76, 8 (2017), 10779–10799.Google ScholarDigital Library
- [19] 2011. A sleep pattern analysis and visualization system to support people with early dementia [C]. The 5th International Conference on Pervasive Computing Technologies for Healthcare and Workshops. 510–513.Google Scholar
- [20] . 2018. RiSH: A robot-integrated smart home for elderly care [J]. Robotics and Autonomous Systems 101 (2018), 74–92.Google ScholarCross Ref
- [21] . 2020. The impact of assistive software application to facilitate people with dementia through participatory research [J]. International Journal of Human-Computer Studies (2020), 143, 102471.Google Scholar
- [22] . 2020. Elderly fall detection based on multi-stream deep convolutional networks [C]. Multimedia Tools and Applications, Vol. 79. 19537–19560.Google ScholarDigital Library
- [23] Adriano Tramontano, Mario Scala, and Mario Magliulo. 2019. Wearable devices for health-related quality of life evaluation [J]. Soft Computing Volume 23, 19 (2019), 9315--9326.Google Scholar
- [24] 2016. Adaptive weighted imbalance learning with application to abnormal activity recognition [J].Neurocomputing 173, 3 (2016), 1927–1935.Google ScholarDigital Library
- [25] 2007. Algorithm to automatically detect abnormally long periods of inactivity in a home [C]. The 1st ACM SIGMOBILE International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments. 89–94.Google Scholar
- [26] 2016. Detecting health and behavior change by analyzing smart home sensor data [C]. IEEE International Conference on Smart Computing. 1–3.Google Scholar
- [27] 2018. Anomaly detection in the elderly daily behavior [C]. The 14th International Conference on Intelligent Environments. 103–106.Google Scholar
- [28] 2018. Technological solutions for older people with Alzheimer's disease: Review [J]. Current Alzheimer Research 15, 10 (2018), 975–983.Google ScholarCross Ref
- [29] 2020. Event-driven framework for detecting unusual patterns in AAL environments [C]. IEEE 33rd International Symposium on Computer-Based Medical Systems. 309–314.Google Scholar
- [30] . 2021. Indirectly supervised anomaly detection of clinically meaningful health events from smart home data [J]. ACM Transactions on Intelligent Systems and Technology. 12 (2021), 1–18.Google ScholarDigital Library
- [31] 2006. A planning system based on Markov decision processes to guide people with dementia through activities of daily living [J]. IEEE Transactions on Information Technology in Biomedicine 10 (2006), 323–333.Google ScholarDigital Library
- [32] 2019. Probabilistic analysis of abnormal behaviour detection in activities of daily living [C]. IEEE International Conference on Pervasive Computing and Communications Workshops. 461–466.Google Scholar
- [33] . 2004. The efficacy of an intelligent cognitive orthosis to facilitate handwashing by persons with moderate to severe dementia [J]. Neuropsychological Rehabilitation 14 (2004), 135–171.Google ScholarCross Ref
- [34] Salvatore Naddeo, Laura Verde, Manolo Forastiere, et al. 2017. A real-time m-healthmonitoring system: An integrated solution combining the use of several wearable sensors and mobile devices [C]. In International Joint Conference on Biomedical Engineering Systems and Technologies. 545--552.Google Scholar
- [35] 2010. Multimodal multitask deep learning model for Alzheimer's disease progression detection based on time series data [J]. Neurocomputing 412 (2010), 197–215.Google ScholarCross Ref
- [36] 2019. Fall detection and 3-D indoor localization by a custom RFID reader embedded in a smart e-health platform [J]. IEEE Transactions on Microwave Theory and Techniques 67, 12 (2019), 5329–5339.Google ScholarCross Ref
- [37] . Detection of abnormal behaviour for dementia sufferers using convolutional neural networks [J]. Artificial Intelligence in Medicine 94 (2019), 88–95.Google ScholarDigital Library
- [38] 2014. A novel stereo positioning method based on optical and SAR sensor [C], IEEE Geoscience and Remote Sensing Symposium. 339–342.Google Scholar
- [39] . 2017. Towards the quantification of human-robot imitation using wearable inertial sensors [C]. In 12th Annual ACM/IEEE International Conference on Human-Robot Interaction. 327–328.Google Scholar
- [40] . 2021. Indirectly supervised anomaly detection of clinically meaningful health events from smart home data [J]. ACM Transactions on Intelligent Systems and Technology 12 (2021), 1–18.Google ScholarDigital Library
- [41] 2020. IoT-based healthcare support system for Alzheimer's patients [J]. Wireless Communications and Mobile Computing (2020), 1–15.Google Scholar
- [42] . 2004. On probabilistic computation tree logic [J]. Validation of Stochastic Systems (2004), 147–188.Google ScholarCross Ref
- [43] . 2008. Principles of Model Checking [M]. MIT Press, 745−907.Google Scholar
Index Terms
- Applying Probabilistic Model Checking to the Behavior Guidance and Abnormality Detection for A-MCI Patients under Wireless Sensor Network
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