Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms

Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance

Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers

Objectives: The main objective of this study was to develop and test classification algorithms based on machine learning using accelerometers to identify the activity type performed by manual wheelchair users with spinal cord injury (SCI). Setting: The study was conducted in the Physical Therapy department and the Physical Education and Sports department of the University of Valencia. Methods: A total of 20 volunteers were asked to perform 10 physical activities, lying down, body transfers, moving items, mopping, working on a computer, watching TV, arm-ergometer exercises, passive propulsion, slow propulsion and fast propulsion, while fitted with four accelerometers placed on both wrists, chest and waist. The activities were grouped into five categories: sedentary, locomotion, housework, body transfers and moderate physical activity. Different machine learning algorithms were used to develop individual and group activity classifiers from the acceleration data for different combinations of number and position of the accelerometers. Results: We found that although the accuracy of the classifiers for individual activities was moderate (55-72%), with higher values for a greater number of accelerometers, grouped activities were correctly classified in a high percentage of cases (83.2-93.6%). Conclusions: With only two accelerometers and the quadratic discriminant analysis algorithm we achieved a reasonably accurate group activity recognition system (490%). Such a system with the minimum of intervention would be a valuable tool for studying physical activity in individuals with SCI.X Garcia-Masso gratefully acknowledges the support of the University of Valencia under project UV-INV-PRECOMP13-115364.García-Massó, X.; Serra-Añó P.; Gonzalez, L.; Ye Lin, Y.; Prats-Boluda, G.; Garcia Casado, FJ. (2015). Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers. Spinal Cord. 53(10):772-777. https://doi.org/10.1038/sc.2015.81S7727775310Buchholz AC, Martin Ginis KA, Bray SR, Craven BC, Hicks AL, Hayes KC et al. Greater daily leisure time physical activity is associated with lower chronic disease risk in adults with spinal cord injury. Appl Physiol Nutr Metab 2009; 34: 640–647.Hetz SP, Latimer AE, Buchholz AC, Martin Ginis KA . Increased participation in activities of daily living is associated with lower cholesterol levels in people with spinal cord injury. Arch Phys Med Rehabil 2009; 90: 1755–1759.Manns PJ, Chad KE . Determining the relation between quality of life, handicap, fitness, and physical activity for persons with spinal cord injury. Arch Phys Med Rehabil 1999; 80: 1566–1571.Serra-Añó P, Pellicer-Chenoll M, García-Massó X, Morales J, Giner-Pascual M, González L-M . Effects of resistance training on strength, pain and shoulder functionality in paraplegics. Spinal Cord 2012; 50: 827–831.Slater D, Meade MA . Participation in recreation and sports for persons with spinal cord injury: review and recommendations. NeuroRehabilitation 2004; 19: 121–129.Lee M, Zhu W, Hedrick B, Fernhall B . Determining metabolic equivalent values of physical activities for persons with paraplegia. Disabil Rehabil 2010; 32: 336–343.Lee M, Zhu W, Hedrick B, Fernhall B . Estimating MET values using the ratio of HR for persons with paraplegia. Med Sci Sports Exerc 2010; 42: 985–990.Hayes AM, Myers JN, Ho M, Lee MY, Perkash I, Kiratli BJ . Heart rate as a predictor of energy expenditure in people with spinal cord injury. J Rehabil Res Dev 2005; 42: 617–624.Washburn RA, Zhu W, McAuley E, Frogley M, Figoni SF . The physical activity scale for individuals with physical disabilities: development and evaluation. Arch Phys Med Rehabil 2002; 83: 193–200.Ginis KAM, Latimer AE, Hicks AL, Craven BC . Development and evaluation of an activity measure for people with spinal cord injury. Med Sci Sports Exerc 2005; 37: 1099–1111.Khan AM, Lee Y-K, Lee S, Kim T-S . Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly. Med Biol Eng Comput 2010; 48: 1271–1279.Khan AM, Lee Y-K, Lee SY, Kim T-S . A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans Inf Technol Biomed Publ 2010; 14: 1166–1172.Liu S, Gao RX, John D, Staudenmayer J, Freedson PS . SVM-based multi-sensor fusion for free-living physical activity assessment. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc 2011; 2011: 3188–3191.Liu S, Gao RX, John D, Staudenmayer JW, Freedson PS . Multisensor data fusion for physical activity assessment. IEEE Trans Biomed Eng 2012; 59: 687–696.Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P . An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 2009; 107: 1300–1307.Trost SG, Wong W-K, Pfeiffer KA, Zheng Y . Artificial neural networks to predict activity type and energy expenditure in youth. Med Sci Sports Exerc 2012; 44: 1801–1809.David Apple MD . Pain above the injury level. Top Spinal Cord Inj Rehabil 2001; 7: 18–29.Subbarao JV, Klopfstein J, Turpin R . Prevalence and impact of wrist and shoulder pain in patients with spinal cord injury. J Spinal Cord Med 1995; 18: 9–13.Postma K, van den Berg-Emons HJG, Bussmann JBJ, Sluis TAR, Bergen MP, Stam HJ . Validity of the detection of wheelchair propulsion as measured with an Activity Monitor in patients with spinal cord injury. Spinal Cord 2005; 43: 550–557.Hiremath SV, Ding D, Farringdon J, Vyas N, Cooper RA . Physical activity classification utilizing SenseWear activity monitor in manual wheelchair users with spinal cord injury. Spinal Cord 2013; 51: 705–709.Itzkovich M, Gelernter I, Biering-Sorensen F, Weeks C, Laramee MT, Craven BC et al. The Spinal Cord Independence Measure (SCIM) version III: reliability and validity in a multi-center international study. Disabil Rehabil 2007; 29: 1926–1933.García-Massó X, Serra-Añó P, García-Raffi LM, Sánchez-Pérez EA, López-Pascual J, Gonzalez LM . Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheelchair users with spinal cord injury. Spinal Cord 2013; 51: 898–903.Preece SJ, Goulermas JY, Kenney LPJ, Howard D . A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng 2009; 56: 871–879.Hurd WJ, Morrow MM, Kaufman KR . Tri-axial accelerometer analysis techniques for evaluating functional use of the extremities. 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Profiling Movement Quality Characteristics of Children (9-11y) During Recess

Introduction. Frequency spectrum characteristics derived from raw accelerometry, such as spectral purity, have the potential to reveal detailed information about children’s movement quality, but remain unexplored in children’s physical activity. The aim of this study was to investigate and profile children’s recess physical activity and movement quality using a novel analytical approach. Materials and Methods. A powered sample of twenty-four children (18 boys) (10.5±0.6y, 1.44±0.09m, 39.6±9.5kg, body mass index; 18.8±3.1 kg.m2) wore an ankle-mounted accelerometer during school recess, for one school-week. Hierarchical clustering, Spearman’s rho and the Mann-Whitney U test were used to assess relationships between characteristics, and to assess inter-day differences. Results. There were no significant inter-day differences found for overall activity (P>0.05), yet significant differences were found for spectral purity derived movement quality (P 0.05), sin embargo, se encontraron diferencias significativas para la calidad del movimiento derivado de la pureza espectral (P <0.001). La actividad global se agrupó jerárquicamente y se correlacionó positivamente con la pureza espectral (P <0,05). Discusión. Este es el primer estudio que informa la pureza espectral de la calidad del movimiento derivado de la actividad física de los niños, en un entorno no controlado y nuestros resultados destacan el potencial para la investigación futura

A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors

Due to the many beneficial effects on physical and mental health and strong association with many fitness and rehabilitation programs, physical activity (PA) recognition has been considered as a key paradigm for internet of things (IoT) healthcare. Traditional PA recognition techniques focus on repeated aerobic exercises or stationary PA. As a crucial indicator in human health, it covers a range of bodily movement from aerobics to anaerobic that may all bring health benefits. However, existing PA recognition approaches are mostly designed for specific scenarios and often lack extensibility for application in other areas, thereby limiting their usefulness. In this paper, we attempt to detect more gym physical activities (GPAs) in addition to traditional PA using acceleration, A two layer recognition framework is proposed that can classify aerobic, sedentary and free weight activities, count repetitions and sets for the free weight exercises, and in the meantime, measure quantities of repetitions and sets for free weight activities. In the first layer, a one-class SVM (OC-SVM) is applied to coarsely classify free weight and non-free weight activities. In the second layer, a neural network (NN) is utilized for aerobic and sedentary activities recognition; a hidden Markov model (HMM) is to provide a further classification in free weight activities. The performance of the framework was tested on 10 healthy subjects (age: 30 ± 5; BMI: 25 ± 5.5 kg/ and compared with some typical classifiers. The results indicate the proposed framework has better performance in recognizing and measuring GPAs than other approaches. The potential of this framework can be potentially extended in supporting more types of PA recognition in complex applications

Profiling Movement Quality Characteristics of Children (9-11y) During Recess

Introduction. Frequency spectrum characteristics derived from raw accelerometry, such as spectral purity, have the potential to reveal detailed information about children’s movement quality, but remain unexplored in children’s physical activity. The aim of this study was to investigate and profile children’s recess physical activity and movement quality using a novel analytical approach. Materials and Methods. A powered sample of twenty-four children (18 boys) (10.5±0.6y, 1.44±0.09m, 39.6±9.5kg, body mass index; 18.8±3.1 kg.m2) wore an ankle-mounted accelerometer during school recess, for one school-week. Hierarchical clustering, Spearman’s rho and the Mann-Whitney U test were used to assess relationships between characteristics, and to assess inter-day differences. Results. There were no significant inter-day differences found for overall activity (P>0.05), yet significant differences were found for spectral purity derived movement quality (P<0.001). Overall activity was hierarchically clustered, and positively correlated, with spectral purity (P<0.05). Discussion. This is the first study to report spectral purity derived movement quality of children’s physical activity in an uncontrolled setting and our results highlight potential for future research

A New Method for Multisensor Data Fusion Based on Wavelet Transform in a Chemical Plant

Abstract This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet &quot;db4&quot; functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter for the final state estimation. The recent data are recursively utilized to apply wavelet transform and extract the variance of the updated data, which makes it suitable to be applied to both static and dynamic systems corrupted by noisy environments. The method has suitable performance in state estimation in comparison with the other alternative algorithms. A three-tank benchmark system has been adopted to comparatively demonstrate the performance merits of the method compared to a known algorithm in terms of efficiently satisfying signal-tonoise (SNR) and minimum square error (MSE) criteria

Fusing actigraphy signals for outpatient monitoring

[EN] Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.This work was partially funded by the European Commission: Help4Mood (Contract No. FP7-ICT-2009-4: 248765). E. FusterGarcia acknowledges Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ-12-05693).Fuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015). Fusing actigraphy signals for outpatient monitoring. Information Fusion. 23:69-80. https://doi.org/10.1016/j.inffus.2014.08.003S69802

Detecting prolonged sitting bouts with the ActiGraph GT3X

The ActiGraph has a high ability to measure physical activity; however, it lacks an accurate posture classification to measure sedentary behavior. The aim of the present study was to develop an ActiGraph (waist-worn, 30 Hz) posture classification to detect prolonged sitting bouts, and to compare the classification to proprietary ActiGraph data. The activPAL, a highly valid posture classification device, served as reference criterion. Both sensors were worn by 38 office workers over a median duration of 9 days. An automated feature selection extracted the relevant signal information for a minute-based posture classification. The machine learning algorithm with optimal feature number to predict the time in prolonged sitting bouts (>= 5 and >= 10 minutes) was searched and compared to the activPAL using Bland-Altman statistics. The comparison included optimized and frequently used cut-points (100 and 150 counts per minute (cpm), with and without low-frequency-extension (LFE) filtering). The new algorithm predicted the time in prolonged sitting bouts most accurate (bias <= 7 minutes/d). Of all proprietary ActiGraph methods, only 150 cpm without LFE predicted the time in prolonged sitting bouts non-significantly different from the activPAL (bias <= 18 minutes/d). However, the frequently used 100 cpm with LFE accurately predicted total sitting time (bias <= 7 minutes/d). To study the health effects of ActiGraph measured prolonged sitting, we recommend using the new algorithm. In case a cut-point is used, we recommend 150 cpm without LFE to measure prolonged sitting and 100 cpm with LFE to measure total sitting time. However, both cpm cut-points are not recommended for a detailed bout analysis.NoneAccepte