Abstract
Purpose
Exercise and physical activity is a driving force for mental health. Major challenges in the treatment of psychological diseases are accurate activity profiles and the adherence to exercise intervention programs. We present the development and validation of CHRONACT, a wearable realtime activity tracker based on inertial sensor data to support mental health.
Methods
CHRONACT comprised a Human Activity Recognition (HAR) algorithm that determined activity levels based on their Metabolic Equivalent of Task (MET) with sensors on ankle and wrist. Special emphasis was put on wearability, real-time data analysis and runtime to be able to use the system as augmented feedback device. For the development, data of 47 healthy subjects performing clinical intervention program activities were collected to train different classification models. The most suitable model according to the accuracy and processing power tradeoff was selected for an embedded implementation on CHRONACT.
Results
A validation trial (six subjects, 6 h of data) showed the accuracy of the system with a classification rate of 85.6%. The main source of error was identified in acyclic activities that contained activity bouts of neighboring classes. The runtime of the system was more than 7 days and continuous result logging was available for 39 h.
Conclusions
In future applications, the CHRONACT system can be used to create accurate and unobtrusive patient activity profiles. Furthermore, the system is ready to assess the effects of individual augmented feedback for exercise adherence.
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References
World Health Organization. Global Recommendations on Physical Activity for Health. WHO Press. 2010. 1–58.
Rethorst CD, Wipfli BM, Landers DM. The antidepressive effects of exercise: a meta-analysis of randomized trials. Sports Med. 2009; 39(6):491–511.
Martinsen EW. Benefits of exercise for the treatment of depression. Sports Med. 1990; 9(6):380–9.
Dunn AL, Jewell JS. The effect of exercise on mental health. Curr Sports Med Rep. 2010; 9(4):202–7.
Mata J, Thompson RJ, Jaeggi SM, Buschkuehl M, Jonides J, Gotlib IH. Walk on the bright side: physical activity and affect in major depressive disorder. J Abnorm Psychol. 2012; 121(2):297–308.
Brosse AL, Sheets ES, Lett HS, Blumenthal JA. Exercise and the treatment of clinical depression in adults. Sports Med. 2002; 32(12):741–60.
Jerome GJ, Rohm Young D, Dalcin A, Charleston J, Anthony C, Hayes J, Daumit GL. Physical activity levels of persons with mental illness attending psychiatric rehabilitation programs. Schizophr Res. 2009; 108(1–3):252–7.
Blumenthal JA, Smith PJ, Hoffman BM. Is exercise a viable treatment for depression? ACSMs Health Fit J. 2012; 16(4):14–21.
Matta Mello Portugal E, Cevada T, Sobral Monteiro-Junior R, Teixeira Guimarães T, da Cruz Rubini E, Lattari E, Blois C, Camaz Deslandes A. Neuroscience of exercise: from neurobiology mechanisms to mental health. Neuropsychobiology. 2013; 68(1):1–14.
Raglin JS. Exercise and mental health. Beneficial and detrimental effects. Sports Med. 1990; 9(6):323–9.
Leutheuser H, Schuldhaus D, Eskofier BM. Hierarchical, multisensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PLoS One. 2013; 8(10):e75196.
Mathie MJ, Celler BG, Lovell NH, Coster AC. Classification of basic daily movements using a triaxial accelerometer. Med Biol Eng Comput. 2004; 42(5):679–87.
Reiss A, Stricker D. Aerobic activity monitoring: towards a long-term approach. Univ Access Inf Soc. 2014; 13(1):101–14.
Reiss A. Personalized mobile physical activity monitoring for everyday life. PhD thesis, Technical University of Kaiserslauten Germany, 2014.
Reiss A, Stricker D, Lamprinos I. An integrated mobile system for long-term aerobic activity monitoring and support in daily life. Conf Proc IEEE Trust Secur Priv Comput Commun. 2012; 1:2021–8.
Ermes M, Pärkkä J, Mäntyjärvi J, Korhonen I. Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans Inf Technol Biomed. 2008; 12(1):20–6.
Bulling A, Blanke U, Schiele B. A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv. 2014; 46(3):33.
Wang C, Lu W, Narayanan MR, Redmond SJ, Lovell NH. Lowpower technologies for wearable telecare and telehealth systems: a review. Biomed Eng Lett. 2015; 5(1):1–9.
Sigrist R, Rauter G, Riener R, Wolf P. Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review. Psychon Bull Rev. 2013; 20(1):21–53.
Callaghan P. Exercise: a neglected intervention in mental health care?. J Psychiatr Ment Health Nurs. 2004; 11(4):476–83.
Knöchel C, Oertel-Knöchel V, O’Dwyer L, Prvulovic D, Alves G, Kollmann B, Hampel H. Cognitive and behavioural effects of physical exercise in psychiatric patients. Prog Neurobiol. 2012; 96(1):46–68.
Cooney GM, Dwan K, Greig CA, Lawlor DA, Rimer J, Waugh FR, McMurdo M, Mead GE. Exercise for depression. Cochrane Database Syst Rev. 2013; 9:CD0043566. doi: 10.1002/14651858.CD004366.pub6.
Avci A, Bosch S, Marin-Perianu M, Marin-Perianu R, Havinga P. Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. Conf Proc Archit Comput Syst. 2010; 1:1–10.
Lara OD, Labrador MA. A survey on human activity recognition using wearable sensors. Commun Surv Tutor. 2013; 15(3):1192–209.
Zijlstra A, Mancini M, Chiari L, Zijlstra W. Biofeedback for training balance and mobility tasks in older populations: a systematic review. J Neuroeng Rehabil. 2010; 7:58.
Dogan-Aslan M, Nakipoglu-Yüzer GF, Dogan A, Karabay I, Ozgirgin N. The effect of electromyo-graphic biofeedback treatment in improving upper extremity functioning of patients with hemiplegic stroke. J Stroke Cerebrovasc Dis. 2012; 21(3):187–92.
Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans Inf Technol Biomed. 2006; 10(1):156–67.
Albu D, Lukkien J, Verhoeven R. On-node processing of ECG signals. Conf Proc IEEE Consum Commun Netw. 2010; 1:1–5.
Ghasemzadeh H, Ostadabbas S, Guenterberg E, Pantelopoulos A. Wireless medical-embedded systems: a review of signalprocessing techniques for classification. IEEE Sens J. 2013; 13(2):423–37.
Hanson MA, Powell HC, Barth AT, Lach J. Application-focused energy-fidelity scalability for wireless motion-based health assessment. ACM TECS. 2012; 11(S2):50.
Strath SJ, Kaminsky LA, Ainsworth BE, Ekelund U, Freedson PS, Gary RA, Richardson CR, Smith DT, Swartz AM. Guide to the assessment of physical activity: Clinical and research applications–a scientific statement from the american heart association. Circulation. 2013; 128(20):2259–79.
Bouten CVC, Koekkoek KTM, Verduin M, Kodde R, Janssen JD. A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans Biomed Eng. 1997; 44(3):136–47.
Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DJ, Tudor-Locke C, Greer JL, Vezina J, Whitt-Glover MC, Leon AS. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011; 43(8):1575–81.
Mohd Nordin INA, Chee PS, Mohd Addi M, Che Harun FK. EZ430-Chronos watch as a wireless health monitoring device. Conf Proc Biomed Eng. 2011; 35:305–7.
Burns A, Greene BR, McGrath MJ, O’Shea TJ, Kuris B, Ayer SM, Stroiescu F, Cionca V. ShimmerTM-a wireless sensor platform for noninvasive biomedical research. IEEE Sens J. 2010; 10(9):1527–34.
Texas Instruments Inc. EZ430-Chronos. In: Texas Instruments Wiki. 2014. http://processors.wiki.ti.com/index.php/Main_Page. Accessed 30 Jul 2014.
Leutheuser H, Doelfel S, Schuldhaus D, Reinfelder S, Eskofier BM. Performance comparison of two step segmentation algorithms using different step activities. Conf Proc Wearable Implant Body Sens Netw. 2014; 1:143–8.
Friedrich-Alexander-Universität Erlangen-Nüurnberg BaSA–Basic Step Activities}. : ActivityNet Benchmark Datasets. 2014. http://www5.cs.fau.de/activitynet/benchmark-datasets/basa-basic-step-activities. Accessed 30 Jul 2014.
Ring M, Jensen U, Kugler P, Eskofier B. Software-based performance and complexity analysis for the design of embedded classification systems. Conf Proc Pattern Recognit. 2012; 1:2266–9.
Jensen U, Ring M, Eskofier B. Generic features for biosignal classification. Sportinformatik. 2012. 162–8.
Knuth DE. The Art of Computer Programming, Volume 2: Seminumerical Algorithms. 3rd ed. Boston: Addison-Wesley Professional. 1997.
Pébay P. Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments. Technical Report SAND2008–6212, Sandia National Laboratories. Livermore, USA. 2008.
Theodoridis S, Koutroumbas K. 4th ed. Waltham: Academic Press; 2008.
Polikar R. Bootstrap-inspired techniques in computational intelligence: ensemble of classifiers, incremental learning, data fusion and missing features. IEEE Signal Process Mag. 2007; 24(4):59–72.
Witten IH, Frank E, Hall MA. Data Mining–Practical Machine Learning Tools and Techniques, 3rd ed. Burlington Morgan Kaufmann; 2011.
Duda RO, Hart PE, Stork DG. Pattern Classification. 2nd ed. Hoboken Wiley-Interscience; 2000.
Smith SW. The scientist and engineer’s guide to digital signal processing. 1st ed. Thousand Oaks California Technical Pub; 1997.
Gordon R. A calculated look at fixed-point arithmetic. Embedded Systems Programming. 1998; 11(4):72–9.
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Jensen, U., Leutheuser, H., Hofmann, S. et al. A wearable real-time activity tracker. Biomed. Eng. Lett. 5, 147–157 (2015). https://doi.org/10.1007/s13534-015-0184-0
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DOI: https://doi.org/10.1007/s13534-015-0184-0