- Email: [email protected]
Computational methods for the automatic classification of postures and movements from acceleration data
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Abstracts / Gait & Posture 30S (2009) S26–S74 Table 1 Movements/postures, feature and one-shot classification algorithms.
Fig. 2. An example of the BF muscle belly trajectories over the (x,y), (y,z) and (x,z) planes, at different stimulation frequencies (A = 16 Hz, B = 25 Hz, C = 40 Hz, D = 50 Hz). Fig.2 corresponds to P1 positioning.
Postures and movements
Sit Lye Stand Walk Stairs Run Bike
Feature
DC component Energy Entropy Correlation coefficients
One-shot classification algorithms
k-NN Parzen GMM Naive Bayesian Support Vector Machine
Table 2 One-shot classifiers and mean classification accuracy. k-NN (k = 1)
Parzen
GMM (M = 1)
Naive Bayesian
Support Vector Machine
99.5%
95.1%
91.0%
97.6%
99.0%
References [1] Fratini A, et al. Relevance of motion artifact in electromyography recordings during vibration treatment. J Electromyogr Kinesiol 2008, doi:10.1016/j.jelekin.2008.04.005. [2] De Talhouet H, Webster JG. The origin of skin-stretch-caused motion artifact under electrodes. Physiol Meas 1996;17(2):81–93. [3] Fratini A, Bifulco P, Cesarelli M, Pasquariello G, Romano M, La Gatta A. Correspondence between Muscle Motion and EMG Activity during Whole Body Vibration IFMBE Proc. In: 4th European Conference of the International Federation for Medical and Biological Engineering, vol. 22. 2008. p. 2069–72. [4] Harazin B, Grzesik J. The transmission of vertical whole body vibration to the body segment of standing subjects. J Sound Vibr 1998;215(4):775–87.
doi:10.1016/j.gaitpost.2009.07.067 Computational methods for the automatic classification of postures and movements from acceleration data A. Mannini ∗ , A.M. Sabatini ARTS Lab - Scuola Superiore Sant’Anna, Pisa, Italy Introduction: The aim of this study is the development of an algorithm for automatic classification of human postures and movements, starting from accelerometer data. The acceleration data can be measured by a few sensors affixed to selected points of the human body. Movement classifiers can be interesting in applications of pervasive computing, whereas contextual awareness may ease the human-machine interaction, or in biomedicine, whereas wearable systems are developed for long-term monitoring of physiological and biomechanical parameters. In this paper we intend to study one-shot and sequential classifiers. One-shot classifiers deliver their actual outcome, without any regard to previous outcomes. Conversely, sequential classifiers, i.e. Hidden Markov Model (HMM), incorporate the statistical information acquired about the movement dynamics into the classification process. Method: Our study revolves around the data-set of human posture/movement primitives described in [1]. Acceleration data, which are sampled at 76 Hz, come from 5 biaxial accelerometers (hip, wrist, arm, ankle, thigh) worn by 20 subjects, who perform several motor activities. Among them, we select the ones reported in Table 1. Feature vectors are obtained using the parameters included in Table 1, by processing time windows composed of 512 samples. A forward selection method based on the k-Nearest
Neighbour (k-NN) rule is used to reduce the feature space dimensionality to d = 17. A test-set of complex movements is created by joining, for each subject, posture/movement primitives extracted from the reduced dataset, in accordance with a fixed transition probability matrix (TPM) (surrogate data). Moreover, primitives of posture/movements unknown to the classifier are randomly inserted into the surrogate data, in proportion 1–3 (max.). Feature vectors in the absence of spurious data are classified using the one-shot algorithms reported in Table 1 [1]. The sequential classifier is an HMM with 7 states. The probability density functions for all d-dimensional emissions are assumed to be described by a Gaussian Mixture Model (GMM) (M = 1) [2]. The supervised nature of the training-set allows us to correctly initialize the TPM and the parameters of the Gaussian densities for each state. This is made to possibly reduce the difficulty of parameter identification during the training phase of the model (Baum-Welch algorithm). In order to reject spurious data before their incorporation in the estimation of the state sequence, a threshold-based decision is made, based on the emission probability from each state of the model. Results and discussion: One-shot classifiers are tested in the absence of spurious data, yielding the results reported in Table 2. As for the sequential classification, in the absence of spurious data: PHMM = 95.6%; in the presence of spurious data: PHMM = 93.9% (with threshold-based decision); PHMM = 71.3% (without thresholdbased decision). The algorithm for spurious data rejection is characterized by sensibility: 96.4% and specificity: 93.7%. The comparison PHMM vs. PGMM highlights the influence that the information about the movement dynamics (TPM) can have on the achievable classification accuracy. A critical element in the design of a classifier is represented by the value of the n/d ratio (n: number of data in the training set) [2]: the classification performance of the algorithms tested in this work have to be considered surprisingly good in spite that the value n/d ratio is low: n/d = 49/17. Finally, when either the Baum-Welch algorithm or the device for spurious data rejection are applied to incoming data, our simulations yield PHMM approximately 90%, even when the TPM is very badly initialized.
Abstracts / Gait & Posture 30S (2009) S26–S74
References [1] Bao L, et al. Activity recognition from user-annotated acceleration data. Pervasive 2004;3001:1–17. [2] Rabiner LR. A tutorial on Hidden Markov Models and selected applications in speech recognition, Proceedings of the IEEE 1989;77(2):276–286.
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Table 1
Y O
MET-min (kCal)
Work (kJ)
WEC (J kg−1 m−1 )
VO2(vt) (l min−1 )
CMJ (W kg−1 )
STS (s)
MVC (N)
4408 1215
907.8 759.1
2.27 2.39
2.76 1.84
46.08 24.74
1.08 1.29
589.8 481.4
doi:10.1016/j.gaitpost.2009.07.068 Association between physical activity level and mobility in individuals living in a city district: A pilot study L. Laudani 1,∗ , G. Vannozzi 1 , A. Kose 2 , A. Macaluso 1 1
Department of Human Movement and Sport Sciences, University of Rome “Foro Italico”, Rome, Italy 2 Department of Biomedical Sciences, University of Sassari, Sassari, Italy
Introduction: It is known that factors underlying human mobility, such as neuromuscular and cardio-respiratory functions, deteriorate gradually with ageing. Such a decline inexorably affects individual motor capacity and, lastly, quality of life. On the other hand, a crucial factor to preserve and improve the health status is represented by the maintenance of an adequate level of physical activity [1]. In particular, it has been shown that significant benefits can be obtained for the health of an individual by simply accumulating some short periods of moderate activity (such as walking or cycling) during the day [2]. However, the relationship between the level of daily physical activity and the age-related decline of mobility is still unclear to date. The aim of the project is to evaluate the possible relationships among factors underlying human mobility (neuromuscular and cardio-respiratory functions and motor capacity), ageing and daily amount of physical activity in individuals living in a city district. Methods: Two participants, a young male aged 35 and an older male aged 70, attended the laboratory on two different occasions and underwent a whole-day long instrumental monitoring of the physical activity level. During the first session, participants filled the International Physical Activity Questionnaire, which provides a comprehensive evaluation of the amount and type of weekly physical activity; anthropometric characteristics (mass, stature and skinfold thicknesses) were measured; walking energy cost at three speeds (slow, comfortable and fast) on a curvilinear circuit was evaluated by means of a telemetric, portable metabolimeter (K4b2, Cosmed); at the end of this session, participants underwent an incremental test on a cycle ergometer (Excalibur, Lode) until reaching the ventilatory threshold, in order to evaluate cardio-respiratory fitness. During the second session, surface electromyography (Pocket EMG, BTS) was recorded from the vastus lateralis and biceps femoris muscles and the following tasks were performed on a force platform (Bertec Co., 40 cm × 80 cm) to evaluate motor capacity: sit-to-stand, squat and counter-movement jumps; at the end of this session, isometric maximal force of knee extensors and flexors was measured by means of a dynamometer (Kin-Com, Chattanooga). Finally, the level of physical activity was monitored by two wearable, inertial devices (IDEEA MiniSun Inc.; FreeSense, Sensorize s.r.l.), which integrate inclinometers and accelerometers located on the individuals’ body, during the most representative day of the participants’ habits. Results: Selection of representative parameters relative to the young (Y) and the older (O) participants: weekly metabolic minutes (MET-min); mechanical work in 24 h (Work); walking energy cost at comfortable speed (WEC); oxygen uptake at ventilatory threshold (VO2(vt)); peak power during counter-movement jump (CMJ); time of sit-to-stand (STS); maximal isometric force during knee extension (MVC) (Table 1).
Discussion: Preliminary results of the present study allow to assess participants’ mobility and, prospectively, to identify its relation with physical activity level and ageing. Therefore, the whole project is feasible by repeating such measures on a higher number of volunteers and by analysing a higher number of parameters relative to each measure. Techniques of “data mining” will then be used to extract relevant information from a large database. The expected output will be represented by a set of representative patterns to characterize each group (based on differences due to gender, age or fitness level) and by the relationships between several variables of laboratory and field monitoring tests. References [1] Rowe JW, Kahan RL. Successful Aging. New York: Pantheon; 1997. [2] ACSM. The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Med Sci Sports Exerc 1998;30:992–1008.
doi:10.1016/j.gaitpost.2009.07.069 A reverse engineering schema to monitor 3-D control of upper limbs while playing the Wii M. Schmid ∗ , D. Bibbo, S. Conforto, T. D’Alessio Department of Applied Electronics, Roma Tre University, Rome, Italy Introduction: Virtual reality systems are at present a reality in providing subjects recovering from different pathologies with an engaging yet effective means to favor rehabilitation [1]. This paradigm is generally defined as virtual rehabilitation: examples range from highly immersive scenarios, such as the ones provided by CAVE technology [2], to desktop-based visual frameworks, such as the ones provided in Manus or Armeo [3]. The limitations associated with the first ones mainly regard cybersickness, which may prevent effectiveness of the treatment [4], and high costs limiting the use of these systems to research-oriented laboratories. On the other side, software associated with desktop-based virtual reality systems for rehabilitation is usually naïve, and not very engaging, thus limiting patient compliance to the treatment. Looking into providing rehab patients with an engaging yet not over-immersive framework for rehabilitation, commercially available gaming consoles have been recently introduced to mediate rehab programs: namely, WiiTM from Nintendo is being progressively used during physical therapy sessions in diverse contexts both in the pediatric population and in adult stroke survivors. By extracting relevant parameters from a 3-axis accelerometer inserted into the remote control handed by the patient, the console is able to decode the ability of the player to perform the requested task. At present, this gaming console does not provide the therapist with a means to assess the goodness of the motor task requested, except the results of the game itself. A step beyond in this direction would be describing the movement by directly accessing the inertial sensor data encoded by the Wii console: this is the objective of the contribution. Methods: The Wiimote control is equipped with a 3-axis accelerometer (ADXL-330, from Analog Devices® ), housed in the central part of the case, and, when handed during the execution of standard games, it is oriented with the first two axes pointing
Abstracts / Gait & Posture 30S (2009) S26–S74 Table 1 Movements/postures, feature and one-shot classification algorithms.
Fig. 2. An example of the BF muscle belly trajectories over the (x,y), (y,z) and (x,z) planes, at different stimulation frequencies (A = 16 Hz, B = 25 Hz, C = 40 Hz, D = 50 Hz). Fig.2 corresponds to P1 positioning.
Postures and movements
Sit Lye Stand Walk Stairs Run Bike
Feature
DC component Energy Entropy Correlation coefficients
One-shot classification algorithms
k-NN Parzen GMM Naive Bayesian Support Vector Machine
Table 2 One-shot classifiers and mean classification accuracy. k-NN (k = 1)
Parzen
GMM (M = 1)
Naive Bayesian
Support Vector Machine
99.5%
95.1%
91.0%
97.6%
99.0%
References [1] Fratini A, et al. Relevance of motion artifact in electromyography recordings during vibration treatment. J Electromyogr Kinesiol 2008, doi:10.1016/j.jelekin.2008.04.005. [2] De Talhouet H, Webster JG. The origin of skin-stretch-caused motion artifact under electrodes. Physiol Meas 1996;17(2):81–93. [3] Fratini A, Bifulco P, Cesarelli M, Pasquariello G, Romano M, La Gatta A. Correspondence between Muscle Motion and EMG Activity during Whole Body Vibration IFMBE Proc. In: 4th European Conference of the International Federation for Medical and Biological Engineering, vol. 22. 2008. p. 2069–72. [4] Harazin B, Grzesik J. The transmission of vertical whole body vibration to the body segment of standing subjects. J Sound Vibr 1998;215(4):775–87.
doi:10.1016/j.gaitpost.2009.07.067 Computational methods for the automatic classification of postures and movements from acceleration data A. Mannini ∗ , A.M. Sabatini ARTS Lab - Scuola Superiore Sant’Anna, Pisa, Italy Introduction: The aim of this study is the development of an algorithm for automatic classification of human postures and movements, starting from accelerometer data. The acceleration data can be measured by a few sensors affixed to selected points of the human body. Movement classifiers can be interesting in applications of pervasive computing, whereas contextual awareness may ease the human-machine interaction, or in biomedicine, whereas wearable systems are developed for long-term monitoring of physiological and biomechanical parameters. In this paper we intend to study one-shot and sequential classifiers. One-shot classifiers deliver their actual outcome, without any regard to previous outcomes. Conversely, sequential classifiers, i.e. Hidden Markov Model (HMM), incorporate the statistical information acquired about the movement dynamics into the classification process. Method: Our study revolves around the data-set of human posture/movement primitives described in [1]. Acceleration data, which are sampled at 76 Hz, come from 5 biaxial accelerometers (hip, wrist, arm, ankle, thigh) worn by 20 subjects, who perform several motor activities. Among them, we select the ones reported in Table 1. Feature vectors are obtained using the parameters included in Table 1, by processing time windows composed of 512 samples. A forward selection method based on the k-Nearest
Neighbour (k-NN) rule is used to reduce the feature space dimensionality to d = 17. A test-set of complex movements is created by joining, for each subject, posture/movement primitives extracted from the reduced dataset, in accordance with a fixed transition probability matrix (TPM) (surrogate data). Moreover, primitives of posture/movements unknown to the classifier are randomly inserted into the surrogate data, in proportion 1–3 (max.). Feature vectors in the absence of spurious data are classified using the one-shot algorithms reported in Table 1 [1]. The sequential classifier is an HMM with 7 states. The probability density functions for all d-dimensional emissions are assumed to be described by a Gaussian Mixture Model (GMM) (M = 1) [2]. The supervised nature of the training-set allows us to correctly initialize the TPM and the parameters of the Gaussian densities for each state. This is made to possibly reduce the difficulty of parameter identification during the training phase of the model (Baum-Welch algorithm). In order to reject spurious data before their incorporation in the estimation of the state sequence, a threshold-based decision is made, based on the emission probability from each state of the model. Results and discussion: One-shot classifiers are tested in the absence of spurious data, yielding the results reported in Table 2. As for the sequential classification, in the absence of spurious data: PHMM = 95.6%; in the presence of spurious data: PHMM = 93.9% (with threshold-based decision); PHMM = 71.3% (without thresholdbased decision). The algorithm for spurious data rejection is characterized by sensibility: 96.4% and specificity: 93.7%. The comparison PHMM vs. PGMM highlights the influence that the information about the movement dynamics (TPM) can have on the achievable classification accuracy. A critical element in the design of a classifier is represented by the value of the n/d ratio (n: number of data in the training set) [2]: the classification performance of the algorithms tested in this work have to be considered surprisingly good in spite that the value n/d ratio is low: n/d = 49/17. Finally, when either the Baum-Welch algorithm or the device for spurious data rejection are applied to incoming data, our simulations yield PHMM approximately 90%, even when the TPM is very badly initialized.
Abstracts / Gait & Posture 30S (2009) S26–S74
References [1] Bao L, et al. Activity recognition from user-annotated acceleration data. Pervasive 2004;3001:1–17. [2] Rabiner LR. A tutorial on Hidden Markov Models and selected applications in speech recognition, Proceedings of the IEEE 1989;77(2):276–286.
S69
Table 1
Y O
MET-min (kCal)
Work (kJ)
WEC (J kg−1 m−1 )
VO2(vt) (l min−1 )
CMJ (W kg−1 )
STS (s)
MVC (N)
4408 1215
907.8 759.1
2.27 2.39
2.76 1.84
46.08 24.74
1.08 1.29
589.8 481.4
doi:10.1016/j.gaitpost.2009.07.068 Association between physical activity level and mobility in individuals living in a city district: A pilot study L. Laudani 1,∗ , G. Vannozzi 1 , A. Kose 2 , A. Macaluso 1 1
Department of Human Movement and Sport Sciences, University of Rome “Foro Italico”, Rome, Italy 2 Department of Biomedical Sciences, University of Sassari, Sassari, Italy
Introduction: It is known that factors underlying human mobility, such as neuromuscular and cardio-respiratory functions, deteriorate gradually with ageing. Such a decline inexorably affects individual motor capacity and, lastly, quality of life. On the other hand, a crucial factor to preserve and improve the health status is represented by the maintenance of an adequate level of physical activity [1]. In particular, it has been shown that significant benefits can be obtained for the health of an individual by simply accumulating some short periods of moderate activity (such as walking or cycling) during the day [2]. However, the relationship between the level of daily physical activity and the age-related decline of mobility is still unclear to date. The aim of the project is to evaluate the possible relationships among factors underlying human mobility (neuromuscular and cardio-respiratory functions and motor capacity), ageing and daily amount of physical activity in individuals living in a city district. Methods: Two participants, a young male aged 35 and an older male aged 70, attended the laboratory on two different occasions and underwent a whole-day long instrumental monitoring of the physical activity level. During the first session, participants filled the International Physical Activity Questionnaire, which provides a comprehensive evaluation of the amount and type of weekly physical activity; anthropometric characteristics (mass, stature and skinfold thicknesses) were measured; walking energy cost at three speeds (slow, comfortable and fast) on a curvilinear circuit was evaluated by means of a telemetric, portable metabolimeter (K4b2, Cosmed); at the end of this session, participants underwent an incremental test on a cycle ergometer (Excalibur, Lode) until reaching the ventilatory threshold, in order to evaluate cardio-respiratory fitness. During the second session, surface electromyography (Pocket EMG, BTS) was recorded from the vastus lateralis and biceps femoris muscles and the following tasks were performed on a force platform (Bertec Co., 40 cm × 80 cm) to evaluate motor capacity: sit-to-stand, squat and counter-movement jumps; at the end of this session, isometric maximal force of knee extensors and flexors was measured by means of a dynamometer (Kin-Com, Chattanooga). Finally, the level of physical activity was monitored by two wearable, inertial devices (IDEEA MiniSun Inc.; FreeSense, Sensorize s.r.l.), which integrate inclinometers and accelerometers located on the individuals’ body, during the most representative day of the participants’ habits. Results: Selection of representative parameters relative to the young (Y) and the older (O) participants: weekly metabolic minutes (MET-min); mechanical work in 24 h (Work); walking energy cost at comfortable speed (WEC); oxygen uptake at ventilatory threshold (VO2(vt)); peak power during counter-movement jump (CMJ); time of sit-to-stand (STS); maximal isometric force during knee extension (MVC) (Table 1).
Discussion: Preliminary results of the present study allow to assess participants’ mobility and, prospectively, to identify its relation with physical activity level and ageing. Therefore, the whole project is feasible by repeating such measures on a higher number of volunteers and by analysing a higher number of parameters relative to each measure. Techniques of “data mining” will then be used to extract relevant information from a large database. The expected output will be represented by a set of representative patterns to characterize each group (based on differences due to gender, age or fitness level) and by the relationships between several variables of laboratory and field monitoring tests. References [1] Rowe JW, Kahan RL. Successful Aging. New York: Pantheon; 1997. [2] ACSM. The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Med Sci Sports Exerc 1998;30:992–1008.
doi:10.1016/j.gaitpost.2009.07.069 A reverse engineering schema to monitor 3-D control of upper limbs while playing the Wii M. Schmid ∗ , D. Bibbo, S. Conforto, T. D’Alessio Department of Applied Electronics, Roma Tre University, Rome, Italy Introduction: Virtual reality systems are at present a reality in providing subjects recovering from different pathologies with an engaging yet effective means to favor rehabilitation [1]. This paradigm is generally defined as virtual rehabilitation: examples range from highly immersive scenarios, such as the ones provided by CAVE technology [2], to desktop-based visual frameworks, such as the ones provided in Manus or Armeo [3]. The limitations associated with the first ones mainly regard cybersickness, which may prevent effectiveness of the treatment [4], and high costs limiting the use of these systems to research-oriented laboratories. On the other side, software associated with desktop-based virtual reality systems for rehabilitation is usually naïve, and not very engaging, thus limiting patient compliance to the treatment. Looking into providing rehab patients with an engaging yet not over-immersive framework for rehabilitation, commercially available gaming consoles have been recently introduced to mediate rehab programs: namely, WiiTM from Nintendo is being progressively used during physical therapy sessions in diverse contexts both in the pediatric population and in adult stroke survivors. By extracting relevant parameters from a 3-axis accelerometer inserted into the remote control handed by the patient, the console is able to decode the ability of the player to perform the requested task. At present, this gaming console does not provide the therapist with a means to assess the goodness of the motor task requested, except the results of the game itself. A step beyond in this direction would be describing the movement by directly accessing the inertial sensor data encoded by the Wii console: this is the objective of the contribution. Methods: The Wiimote control is equipped with a 3-axis accelerometer (ADXL-330, from Analog Devices® ), housed in the central part of the case, and, when handed during the execution of standard games, it is oriented with the first two axes pointing