Anomaly Detection in Activities of Daily Living with Linear Drift

Abstract

Anomalyq detection in Activities of Daily Living (ADL) plays an important role in e-health applications. An abrupt change in the ADL performed by a subject might indicate that she/he needs some help. Another important issue related with e-health applications is the case where the change in ADL undergoes a linear drift, which occurs in cognitive decline, Alzheimer’s disease or dementia. This work presents a novel method for detecting a linear drift in ADL modelled as circular normal distributions. The method is based on techniques commonly used in Statistical Process Control and, through the selection of a convenient threshold, is able to detect and estimate the change point in time when a linear drift started. Public datasets have been used to assess whether ADL can be modelled by a mixture of circular normal distributions. Exhaustive experimentation was performed on simulated data to assess the validity of the change detection algorithm, the results showing that the difference between the real change point and the estimated change point was \(4.90_{+3.17}^{-1.98}\) days on average. ADL can be modelled using a mixture of circular normal distributions. A new method to detect anomalies following a linear drift is presented. Exhaustive experiments showed that this method is able to estimate the change point in time for processes following a linear drift.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. 1.

    Fozard J L, Rietsema J, Bouma H, Graafmans J A M. Gerontechnology: creating enabling environments for the challenges and opportunities of aging. Educ Gerontol 2000;26(4):331–344.

    Google Scholar 

  2. 2.

    Rashidi P, Mihailidis A. A survey on Ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 2013;17(3):579–590.

    PubMed  Google Scholar 

  3. 3.

    Bjering H, Curry J, Maeder A. Gerontechnology: the importance of user participation in ICT development for older adults. Investing in e-health: people, knowledge and technology for a healthy future: selected papers from the 22nd australian national health informatics conference (HIC 2014), 11–14 August 2014, Melbourne Convention and Exhibition Centre; 2014. p. 7–12.

  4. 4.

    Banaee H, Ahmed M U, Loutfi A. Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 2013;13(12):17472–17500.

    CAS  PubMed  Google Scholar 

  5. 5.

    Kim J Y, Liu N, Tan H X, Chu C H. Unobtrusive monitoring to detect depression for elderly with chronic illnesses. IEEE Sensors J 2017;17(17):5694–5704.

    Google Scholar 

  6. 6.

    Igual R, Medrano C, Plaza I. Challenges, issues and trends in fall detection systems. Biomed Eng Online 2013;12(1):66.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Chandola V, Banerjee A, Kumar V. Anomaly detection: a survey. ACM Comput Surv 2009;41(3):15:1–15:58.

    Google Scholar 

  8. 8.

    Lee W, Stolfo S J, et al. Data mining approaches for intrusion detection. USENIX security symposium. San Antonio, TX. 1998. p. 79–93.

  9. 9.

    Atli B G, Miche Y, Kalliola A, Oliver I, Holtmanns S, Lendasse A. 2018. Anomaly-based intrusion detection using extreme learning machine and aggregation of network traffic statistics in probability space. Cognitive Computation.

  10. 10.

    Stoumbos Z G, Reynolds Jr M R, Ryan T P, Woodall W H. The state of statistical process control as we proceed into the 21st century. J Am Stat Assoc 2000;95(451):992–998.

    Google Scholar 

  11. 11.

    Tsui F C, Espino J U, Dato V M, Gesteland P H, Hutman J, Wagner M M. Technical description of RODS: a real-time public health surveillance system. J Am Med Inform Assoc 2003;10(5): 399–408.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Barnett T P, Pierce D W, Schnur R. Detection of anthropogenic climate change in the world’s oceans. Science 2001;292(5515):270–274.

    CAS  PubMed  Google Scholar 

  13. 13.

    Oakland J S. Statistical process control. Evanston: Routledge; 2007.

    Google Scholar 

  14. 14.

    Page E S. Continuous inspection schemes. Biometrika 1954;41(1/2):100–115.

    Google Scholar 

  15. 15.

    Page E S. A test for a change in a parameter occurring at an unknown point. Biometrika 1955; 42(3/4):523–527.

    CAS  Google Scholar 

  16. 16.

    MacDonald S W, Hultsch D F, Dixon R A. Performance variability is related to change in cognition: evidence from the Victoria Longitudinal Study. Psychol Aging 2003;18(3):510.

    PubMed  Google Scholar 

  17. 17.

    Jimison H, Pavel M, McKanna J, Pavel J. Unobtrusive monitoring of computer interactions to detect cognitive status in elders. IEEE Trans Inf Technol Biomed 2004;8(3):248–252.

    PubMed  Google Scholar 

  18. 18.

    Hayes T L, Abendroth F, Adami A, Pavel M, Zitzelberger T A, Kaye J A. Unobtrusive assessment of activity patterns associated with mild cognitive impairment. Alzheimers. Dement. 2008;4 (6):395–405.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Dodge H H, Mattek N C, Austin D, Hayes T L, Kaye J A. In-home walking speeds and variability trajectories associated with mild cognitive impairment. Neurology 2012;78(24):1946–1952.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Boise L, Camicioli R, Morgan D L, Rose J H, Congleton L. Diagnosing dementia: perspectives of primary care physicians. Gerontologist 1999;39(4):457–464.

    CAS  PubMed  Google Scholar 

  21. 21.

    Bradford A, Kunik M E, Schulz P, Williams S P, Singh H. Missed and delayed diagnosis of dementia in primary care: prevalence and contributing factors. Alzheimer Dis Assoc Disord 2009;23(4): 306.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Gaugler J, James B, Johnson T, Scholz K, Weuve J. Alzheimer’s disease facts and figures. Alzheimers Dement 2016;12(4):459–509.

    Google Scholar 

  23. 23.

    Organization WH. 2001. The World Health Report 2001: mental health: new understanding, new hope. World Health Organization.

  24. 24.

    Organization WH. 2015. World report on ageing and health. World Health Organization.

  25. 25.

    Pires P, Mendes L, Mendes J, Rodrigues R, Pereira A. Integrated e-healthcare system for elderly support. Cogn. Comput. 2016;8(2):368–384.

    Google Scholar 

  26. 26.

    Castillo J C, Castro-González Á , Fernández-Caballero A, Latorre J M, Pastor J M, Fernández-Sotos A, et al. Software architecture for smart emotion recognition and regulation of the ageing adult. Cogn. Comput. 2016;8(2):357–367.

    Google Scholar 

  27. 27.

    Alberdi Aramendi A, Weakley A, Aztiria Goenaga A, Schmitter-Edgecombe M, Cook DJ. Automatic assessment of functional health decline in older adults based on smart home data. J Biomed Inform 2018;81:119–130.

    PubMed  Google Scholar 

  28. 28.

    Galambos C, Skubic M, Wang S, Rantz M. Management of dementia and depression utilizing in-home passive sensor data . Gerontechnology: International Journal on the Fundamental Aspects of Technology to Serve the Ageing Society 2013;11(3):457.

    Google Scholar 

  29. 29.

    Jessen F, Amariglio R E, van Boxtel M, Breteler M, Ceccaldi M, Chételat G, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement 2014;10(6):844–852.

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Guarnieri B, Sorbi S. Sleep and cognitive decline: a strong bidirectional relationship. it is time for specific recommendations on routine assessment and the management of sleep disorders in patients with mild cognitive impairment and dementia. Eur Neurol 2015;74(1-2):43–48.

    PubMed  Google Scholar 

  31. 31.

    Hall CB, Lipton RB, Sliwinski M, Stewart WF. A change point model for estimating the onset of cognitive decline in preclinical Alzheimer’s disease. Stat Med 2000;19(11–12):1555–1566.

    CAS  PubMed  Google Scholar 

  32. 32.

    van Someren EJW, Hagebeuk EEO, Lijzenga C, Scheltens P, de Rooij SEJA, Jonker C, et al. Circadian rest—activity rhythm disturbances in Alzheimer’s disease. Biol Psychiatry 1996;40(4): 259–270.

    PubMed  Google Scholar 

  33. 33.

    Schmidt C, Peigneux P, Cajochen C. Age-related changes in sleep and circadian rhythms: impact on cognitive performance and underlying neuroanatomical networks. Front Neurol 2012;3:118.

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Leng Y, Musiek E S, Hu K, Cappuccio F P, Yaffe K. Association between circadian rhythms and neurodegenerative diseases. Lancet Neurol 2019;18(3):307–318.

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Kaye JA, Maxwell SA, Mattek N, Hayes TL, Dodge H, Pavel M, et al. Intelligent systems for assessing aging changes: home-based, unobtrusive, and continuous assessment of aging. J Gerontol B Psychol Sci Soc Sci 2011;66(1):i180–90.

    PubMed  Google Scholar 

  36. 36.

    Keage HAD, Banks S, Yang KL, Morgan K, Brayne C, Matthews FE. What sleep characteristics predict cognitive decline in the elderly? Sleep Med 2012;13(7):886–892.

    PubMed  Google Scholar 

  37. 37.

    Lyons BE, Austin D, Seelye A, Petersen J, Yeargers J, Riley T, et al. Pervasive computing technologies to continuously assess Alzheimer’s disease progression and intervention efficacy. Front Aging Neurosci 2015;7:102.

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Peter-Derex L, Yammine P, Bastuji H, Croisile B. Sleep and alzheimer’s disease. Philadelphia: W.B. Saunders; 2015.

    Google Scholar 

  39. 39.

    Seelye A, Mattek N, Howieson D, Riley T, Wild K, Kaye J. The impact of sleep on neuropsychological performance in cognitively intact older adults using a novel in-home sensor-based sleep assessment approach. The Clinical Neuropsychologist 2015;29(1):53–66.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Devore E E, Grodstein F, Schernhammer E S. Sleep duration in relation to cognitive function among older adults: a systematic review of observational studies. Neuroepidemiology 2016;46(1):57–78.

    PubMed  Google Scholar 

  41. 41.

    Cassidy-Eagle EL, Siebern A. Sleep and mild cognitive impairment. Sleep Science and Practice 2017;1(1):15.

    Google Scholar 

  42. 42.

    Liguori C, Romigi A, Nuccetelli M, Zannino S, Sancesario G, Martorana A, et al. Orexinergic system dysregulation, sleep impairment, and cognitive decline in Alzheimer disease. JAMA Neurology 2014;71(12):1498–1505.

    PubMed  Google Scholar 

  43. 43.

    Mohs RC, Schmeidler J, Aryan M. Longitudinal studies of cognitive, functional and behavioural change in patients with Alzheimer’s disease. Stat Med 2000;19(11–12):1401–1409.

    CAS  PubMed  Google Scholar 

  44. 44.

    Stem RG, Mohs RC, Davidson M, Silverman J, Kramer-ginsberg E, et al. 1994. A Longitudinal Study of Alzheimer’s Disease. Measurement, Rate, and Predictors of Cognitive Deterioration. 390–396.

  45. 45.

    Suzuki T, Murase S. Influence of outdoor activity and indoor activity on cognition decline: use of an infrared sensor to measure activity. Telemed. e-Health 2010;16(6):686–690.

    Google Scholar 

  46. 46.

    Petersen J, Austin D, Mattek N, Kaye J. Time out-of-home and cognitive, physical, and emotional wellbeing of older adults: a longitudinal mixed effects model. PLoS ONE 2015;10(10):e0139643.

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Harada K, Lee S, Lee S, Bae S, Harada K, Suzuki T, et al. Objectively-measured outdoor time and physical and psychological function among older adults. Geriatr Gerontol Int 2016;17 (10):1455–1462.

    PubMed  Google Scholar 

  48. 48.

    Dawadi PN, Cook DJ, Schmitter-Edgecombe M, Parsey C. Automated assessment of cognitive health using smart home technologies. Technol Health Care 2013;21(4):323–343.

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Austin J, Dodge H H, Riley T, Jacobs P G, Thielke S, Kaye J. A smart-home system to unobtrusively and continuously assess loneliness in older adults. IEEE J. Transl. Eng. Health Med.-JTEHM 2016;4:1–11.

    Google Scholar 

  50. 50.

    Kötteritzsch A, Weyers B. Assistive technologies for older adults in urban areas: a literature review. Cogn. Comput. 2016;8(2):299–317.

    Google Scholar 

  51. 51.

    Zarandi M H F, Alaeddini A. A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts. Inf Sci 2010;180(16):3033–3044.

    Google Scholar 

  52. 52.

    López-de Ipiña K, Alonso J B, Solé-Casals J, Barroso N, Henriquez P, Faundez-Zanuy M, et al. On automatic diagnosis of alzheimer’s disease based on spontaneous speech analysis and emotional temperature. Cogn. Comput. 2015;7(1):44–55.

    Google Scholar 

  53. 53.

    Impedovo D, Pirlo G, Vessio G, Angelillo M T. A Handwriting-Based protocol for assessing neurodegenerative dementia. Cogn. Comput.: May; 2019.

    Google Scholar 

  54. 54.

    Shewhart W A. 1931. Economic control of quality of manufactured product. ASQ Quality Press; vol. 509.

  55. 55.

    Roberts S. Control chart tests based on geometric moving averages. Technometrics 1959;1(3): 239–250.

    Google Scholar 

  56. 56.

    Basseville M, Nikiforov I V. Detection of abrupt changes: theory and application. Upper Saddle River: Prentice-Hall, Inc.; 1993.

    Google Scholar 

  57. 57.

    Cheng C S. A multi-layer neural network model for detecting changes in the process mean. Comput Ind Eng 1995;28(1):51–61.

    Google Scholar 

  58. 58.

    Guh R S, Hsieh Y C. A neural network based model for abnormal pattern recognition of control charts. Comput Ind Eng 1999;36(1):97–108.

    Google Scholar 

  59. 59.

    Cheng C S, Cheng S S. A neural network-based procedure for the monitoring of exponential mean. Comput Ind Eng 2001;40(4):309–321.

    Google Scholar 

  60. 60.

    Chang S, Aw C. A neural fuzzy control chart for detecting and classifying process mean shifts. Int J Prod Res 1996;34(8):2265–2278.

    Google Scholar 

  61. 61.

    El-Shal S M, Morris A S. A fuzzy expert system for fault detection in statistical process control of industrial processes. IEEE Trans Syst Man Cybern Part C Appl Rev 2000;30(2):281–289.

    Google Scholar 

  62. 62.

    Alaeddini A, Ghazanfari M, Nayeri M A. A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts. Inf Sci 2009;179(11):1769–1784.

    Google Scholar 

  63. 63.

    Lu K P, Chang S T, Yang M S. Change-point detection for shifts in control charts using fuzzy shift change-point algorithms. Comput Ind Eng 2016;93:12–27.

    Google Scholar 

  64. 64.

    Raftery A, Akman V. 1986. Bayesian analysis of a poisson process with a change-point.

  65. 65.

    Assareh H, Noorossana R, Mengersen K L. Bayesian change point estimation in Poisson-based control charts. J Ind Eng Int 2013;9(1):32.

    Google Scholar 

  66. 66.

    Perry M B, Pignatiello J J, Simpson J R. Estimating the change point of a Poisson rate parameter with a linear trend disturbance. Qual Reliab Eng Int 2006;22(4):371–384.

    Google Scholar 

  67. 67.

    Noorossana R, Shadman A. Estimating the change point of a normal process mean with a monotonic change. Qual Reliab Eng Int 2009;25(1):79–90.

    Google Scholar 

  68. 68.

    Noorossana R, Heydari M. Change point estimation of a process variance with a linear trend disturbance. J Ind Eng Int 2009;2:25–30.

    Google Scholar 

  69. 69.

    Lara O D, Labrador MA. A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 2013;15(3):1192–1209.

    Google Scholar 

  70. 70.

    Guan D, Ma T, Yuan W, Lee Y K, Jehad Sarkar A. Review of sensor-based activity recognition systems. IETE Tech Rev 2011;28(5):418–433.

    Google Scholar 

  71. 71.

    Ke S R, Thuc H L U, Lee Y J, Hwang J N, Yoo J H, Choi K H. A review on video-based human activity recognition. Computers 2013;2(2):88–131.

    Google Scholar 

  72. 72.

    Vrigkas M, Nikou C, Kakadiaris I A. A review of human activity recognition methods. Frontiers in Robotics and AI 2015;2:28.

    Google Scholar 

  73. 73.

    van Kasteren TL, Englebienne G, Kröse BJ. Human activity recognition from wireless sensor network data: benchmark and software. Activity recognition in pervasive intelligent environments. Springer; 2011. p. 165–186.

  74. 74.

    Mardia K V, Jupp P E, Vol. 494. Directional statistics. New York: Wiley; 2009.

    Google Scholar 

  75. 75.

    Diethe T, Twomey N, Flach P. Bayesian modelling of the temporal aspects of smart home activity with circular statistics. Joint european conference on machine learning and knowledge discovery in databases. Springer; 2015. p. 279–294.

  76. 76.

    Chinellato E, Mardia K, Hogg D, Cohn A. An Incremental von Mises Mixture Framework for Modelling Human Activity Streaming Data. International Work-Conference on Time Series Analysis (ITISE); 2017. p. 379–389.

  77. 77.

    Brunsdon C, Corcoran J. Using circular statistics to analyse time patterns in crime incidence. Comput Environ Urban Syst 2006;30(3):300–319.

    Google Scholar 

  78. 78.

    Kubiak T, Jonas C. Applying circular statistics to the analysis of monitoring data. Eur J Psychol Assess 2007;23(4):227–237.

    Google Scholar 

  79. 79.

    Franco C, Demongeot J, Villemazet C, Vuillerme N. Behavioral telemonitoring of the elderly at home: detection of nycthemeral rhythms drifts from location data. 2010 IEEE 24th international conference on advanced information networking and applications workshops. IEEE; 2010. p. 759–766.

  80. 80.

    Jammalamadaka S R, Sengupta A. 2001. Topics in circular statistics. World Scientific, vol. 5.

  81. 81.

    Cremers J, Klugkist I. One direction? A tutorial for circular data using R with examples in cognitive psychology. Front Psychol 2018;9:2040.

    PubMed  PubMed Central  Google Scholar 

  82. 82.

    Puglisi G, Leonetti A, Landau A, Fornia L, Cerri G, Borroni P. The role of attention in human motor resonance. PloS one 2017;12(5):e0177457.

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Warren W H, Rothman D B, Schnapp B H, Ericson J D. Wormholes in virtual space: from cognitive maps to cognitive graphs. Cognition 2017;166:152–163.

    PubMed  Google Scholar 

  84. 84.

    Yin X, Shen W, Samarabandu J, Wang X. Human activity detection based on multiple smart phone sensors and machine learning algorithms. 2015 IEEE 19th international conference on computer supported cooperative work in design (CSCWD); 2015. p. 582–587.

  85. 85.

    Yin J, Yang Q, Pan J J. Sensor-based abnormal human-activity detection. IEEE Trans Knowl Data Eng 2008;20(8):1082–1090.

    Google Scholar 

  86. 86.

    van Kasteren T. L., Englebienne G., Kröse B. J. Human activity recognition from wireless sensor network data: Benchmark and software. Activity recognition in pervasive intelligent environments (pp. 165–186). Atlantis Press.; 2011.

  87. 87.

    Forkan A R M, Khalil I, Tari Z, Foufou S, Bouras A. A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living. Pattern Recogn 2015;48(3):628–641.

    Google Scholar 

  88. 88.

    Zhu C, Sheng W, Liu M. Wearable sensor-based behavioral anomaly detection in smart assisted living systems. IEEE Trans Autom Sci Eng 2015;12:1225–1234.

    Google Scholar 

  89. 89.

    Duong TV, Bui HH, Phung DQ, Venkatesh S. Activity recognition and abnormality detection with the switching hidden semi-markov model. Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) - volume 1 - volume 01. CVPR ’05. Washington, DC, USA: IEEE Computer Society; 2005. p. 838–845.

  90. 90.

    Shin J H, Lee B, Park K S. Detection of abnormal living patterns for elderly living alone using support vector data description. IEEE Trans Inf Technol Biomed 2011;15(3):438–448.

    PubMed  Google Scholar 

  91. 91.

    Palaniappan A, Bhargavi R, Vaidehi V. Abnormal human activity recognition using SVM based approach. 2012 international conference on recent trends in information technology; 2012. p. 97–102.

  92. 92.

    Riboni D, Bettini C, Civitarese G, Janjua Z H, Helaoui R. SmartFABER: recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment. Artif Intell Med 2016; 67(Supplement C):57–74.

    PubMed  Google Scholar 

  93. 93.

    Virone G, Noury N, Demongeot J. A system for automatic measurement of circadian activity deviations in telemedicine. IEEE Trans Biomed Eng 2002;49(12):1463–1469.

    PubMed  Google Scholar 

  94. 94.

    Fahad LG, Rajarajan M. Anomalies detection in smart-home activities. IEEE 14th international conference on machine learning and applications (ICMLA), 2015. IEEE; 2015. p. 419–422.

  95. 95.

    Fisher N. I. 1995. Statistical analysis of circular data. Cambridge University Press.

  96. 96.

    Cook D J, Schmitter-Edgecombe M. Assessing the quality of activities in a smart environment. Methods Inf Med 2009;48(05):480–485.

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Cook D J. Learning setting-generalized activity models for smart spaces. IEEE Intell Syst 2010; 2010(99):1.

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Kullback S, Leibler R A. On information and sufficiency. Ann Math Stat 1951;22(1):79–86.

    Google Scholar 

  99. 99.

    Brookmeyer R, Corrada M M, Curriero F C, Kawas C. Survival following a diagnosis of Alzheimer disease. Arch Neurol 2002;59(11):1764–1767.

    PubMed  Google Scholar 

  100. 100.

    Belmonte-Fernández Ó, Puertas-Cabedo A, Torres-Sospedra J, Montoliu-Colás R, Trilles-Oliver S. An indoor positioning system based on wearables for ambient-assisted living. Sensors 2017;17(1):36.

    Google Scholar 

Download references

Funding

This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities through the “Retos investigación” programme (RTI2018-095168-B-C53) and by the Universitat Jaume I “Pla de promoció de la investigació 2017” programme (UJI-B2017-45). Óscar Belmonte-Fernández had a grant from the Spanish Ministry of Science, Innovation and Universities (PRX18/00123) for developing part of this work.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Óscar Belmonte-Fernández.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Belmonte-Fernández, Ó., Caballer-Miedes, A., Chinellato, E. et al. Anomaly Detection in Activities of Daily Living with Linear Drift. Cogn Comput (2020). https://doi.org/10.1007/s12559-020-09740-6

Download citation

Keywords

  • Anomaly detection
  • Activities of Daily Living
  • Abrupt change
  • Linear drift
  • Circular normal distribution