- Email: [email protected]
Tremor detection and tracking through sEMG analysis
S56
Abstracts / Gait & Posture 30S (2009) S26–S74
Figs. 2 and 3.
Results: Fig. 1 shows an example of how LPF and EMD work on the acceleration signals in the presence of tremor: power spectral density of the raw signal shows high tremor peaks that LPF does not suppress completely. Conversely, EMD-filtered signal does not show significant components after the cutoff frequency. In Figs. 2 and 3 two representative parameters of the set are presented: jerk (a smoothness parameter) and centroidal frequency, both computed in AP during DT trials. Medians of EMD distributions are usually lower than LPF due to the bandwidth reduction. EMD dispersion is slightly decreased, compared to LPF, in control and PD trials without tremor; on the other hand dispersion in PD trials with tremor is substantially cut down by EMD. Discussion: LPF higher dispersion in tremorous trials is caused by the high-frequency components that are not adequately suppressed by the filter. Similar results can be seen for other frequency parameters, while mean acceleration module and rms acceleration module, keep the same distribution with some exceptions in ML direction where the tremor contribution is higher. Difference between LPF and EMD grows in DT condition compared to ST because PD subjects usually exhibit a higher level of tremor performing the cognitive task. These results suggest the choice of EMD filtering instead of LPF in order to have a better tremor-free estimate of posturographic parameters. References [1] G.Rilling et al., IEEE-EURASIP NSIP, Grado, Italy, 2003. [2] de Lima ER, et al. Med Biol Eng Comput 2006;44(7):569–82. [3] Chiari L, et al. Clin Biomech 2002;17(9-10):666–77.
doi:10.1016/j.gaitpost.2009.07.051 Tremor detection and tracking through sEMG analysis S. Conforto ∗ , C. De Marchis, G. Severini, T. D’Alessio Department of Applied Electronics, University Roma TRE, Rome, Italy Introduction: Tremor is a common movement disorder often connected to a neuromuscular disease (i.e. Parkinson, multiple sclerosis) whose incidence is increasing with ageing. Typical treatments, based on drugs, surgery, and deep brain stimulation, are ineffective in almost the 25% of patients thus stressing the need for solutions to reduce tremor. Up to now motion sensors [1] have been used to detect tremor aiming at its mechanical suppression. However, since methods based on inertial data cannot be easily
applied in a real-time framework because of the delay in the control chain, recently the research is trying to integrate inertial and electrophysiological signals such as surface EMG. sEMG seems adequate to detect the movement before its mechanical actuation. Several linear techniques have been used to extract muscular activation timing from sEMG. Unfortunately, they are not always suitable for tremor and often they cannot be implemented in real-time as needed in the context of the active suppression of tremor through neuroprosthesis [2]. The present study deals with the detection and tracking of tremor and its separation from voluntary movement through the use of sEMG signals. Methods: The proposed technique is based on the hypothesis that a tremor burst in a sEMG signal can be modeled as an energy distribution over a short time interval [3]. From a mathematical point of view this distribution can be described by a second order moment function (ti is the center of the window W sliding all over |x(t)| that is the rectified t +W/2 sEMG signal) t +W/2 SOMF(W, ti ) = ( ti =t −W/2 |x(tn )|(tn − ti )2 /( ti =t −W/2 |x(tn )|). The n
i
n
i
function depends on the window dimension: W should contain just one muscular burst at once for proper results. When the repetition frequency ft of the bursts is known the optimal dimension equals W = fc /ft , being fc the sampling rate. The function oscillates around W2 /12 with local minima allocated in the centre of the muscular bursts. Since it has been demonstrated theoretically that both in the onset and the offset of the muscular activation the SOMF equals to W2 /12, this value represents an automatic threshold for the detection of muscular activation. The need of knowing in advance the tremor frequency ft constitutes a drawback of the approach in [3], that we have overcome by considering that (i) tremor frequencies are in the range 3–12 Hz, (ii) SOMF calculated with W = fc /ft detects correctly tremor bursts in a range ±2 Hz around ft . In this way the actual ft , to be used for the window dimensioning, is given by a pre-estimation phase where the detection is made by averaging different SOMFs (calculated over windows covering all the tremor frequencies range). The amplitude of the tremor oscillation can be assessed by estimating the SNR that depends both on SOMF values and burst duration. Results: The main information the improved algorithm can provide are related to: (i) the onset of tremor, (ii) the frequency of tremor, (iii) the detection of muscular activation, (iv) the amplitude estimation. The algorithm has been tested on tremor sEMG signals simulated by amplitude modulating white noise series (SNR range 6–30 dB, tremor frequency range 4-11 Hz). Preliminary results show detection performance (onset and offset of the tremor) with bias lower than 9 ms and standard deviation lower than 12 ms. For SNR lower than 14 dB and for tremor frequency greater than 8 Hz, the algorithm overestimates the SNR, and then the amplitude of tremor, while increasing SNR the bias of the estimation is always lower than 2 dB. Since the algorithm has a low computational cost it could be easily implemented in real time. Discussion: SOMF is a valuable and low cost approach for a nonlinear analysis of sEMG signal. The theoretical proofs guarantee the extraction of the basic parameters of tremor by a simple and automatic thresholding operation (the threshold is objective and dependent on the analysis window dimension that can be adaptively estimated). The performance of the approach in terms of estimation consistency are comparable with the techniques widely used in the literature. Tremor characteristics are properly extracted by sEMG signals without being affected by voluntary movements. The results, even if preliminary, are encouraging and promising for the field of assistive technology for tremor control and for the studies that are in evolution such as the one presented in the European Project TREMOR (FP7 action ICT-2007.7.2 “Accessible and Inclusive ICT”, grant number ICT-2007-224051).
Abstracts / Gait & Posture 30S (2009) S26–S74
References [1] Riviere C, Thakor N. IEEE Eng Med Biol 1996;15(3):29–36. [2] Prochazka A, Gillard D, Bennett DJ. J Neurophysiol 1997;77:3237–51. [3] Journée HL, Postma AA, Sunc M, Staal MJ. Med Eng Phys 2008;30:75–83.
doi:10.1016/j.gaitpost.2009.07.052 Motor task manager and upper limb reaching task: Validation of movement analysis M.B. Warner 1 , A. Pittaluga 2,∗
Novellino 2 , M.J.
Stokes 1 , A.
Astill 1 , E.
1
School of Health Sciences, University of Southampton, UK 2 ett S.r.l., Genova, Italy
Introduction: Motor dysfunction as a result of neurological disorders, such as Parkinson’s disease and Multiple Sclerosis, is evident when performing goal orientated reaching tasks [1,2]. Standardising movement and reaching tasks are difficult however, due to the large number of degrees of freedom in the upper limb. The motor tasks manager (MTM) (ett; Genoa, Italy) has been developed to provide clinicians and researchers with a standardised, objective reaching task. Before the system can be used in a clinical environment the validity of measurements provided by the MTM must be assessed Aim: To compare MTM measurements to an established and valid motion tracking system. Methods: Participants: Nine healthy participants ages 24–55 years (mean = 31.4, SD ± 9.84) performed a goal orientated reaching task prescribed by the MTM software. Reaching task: Participants were seated at a table and moved a computer mouse horizontally across a digitising tablet on the table surface (Fig. 1a). The task involved moving a cursor on a computer screen via the digitising tablet and mouse to 8 targets presented radially around a centre start point (Fig. 1b). Each target was presented sequentially, starting at the 3 o’clock position moving anticlockwise, at intervals of 1.8 s. Motion analysis: • MTM: The MTM recorded the 2-dimensional movements of the mouse across the digitising tablet at 200 Hz. • Vicon: Three dimensional wrist movement was captured using a 6 camera Vicon 460 motion capture system (Vicon; Oxford, UK) at a sampling frequency of 200 Hz. Markers were placed on the ulnar and radial styloids and the wrist local coordinate system was defined according to the International of Society Biomechanics guidelines [3] (Fig. 2). Path length (starting point to reversal point) and peak velocity were calculated from the cursor movements of the MTM and wrist movements from the Vicon simultaneously during the MTM reaching task. Results: Path length showed good agreement between MTM and Vicon measurements with both motion tracking methods providing path lengths that were close to the expected path length of 10 cm (distance from centre start point to target). Peak velocity showed a difference between MTM and Vicon tracking methods. Typically the wrist is used for the calculation of reaching task velocity [4]. The position of the MTM mouse, the point where the MTM records movement of the arm, however is positioned beneath the hand. The offset distance between the MTM mouse and ulnar styloid (origin of the wrist) would account for the difference in velocity, particularly if the participant followed a non-linear trajectory towards the target. The MTM mouse would follow an
S57
increased radius, compared to the wrist, and result in an increased velocity. Discussion: The MTM provides valid measurements for path length and records a higher value for peak velocity when compared to that measured by a standard measurement protocol. Therefore caution will be needed when comparing MTM results for velocity to those from other motion tracking devices. Further investigations to better understand the recorded difference are needed as well.
References [1] [2] [3] [4]
Ghilardi MF et al., Brain Research, 876, 112–123. Longstaff MG et al., Human Movement Science, 25, 474–491. Wu G et al., Journal of Biomechanics. 38: 981–992. Jones LA, et al. Human hand function. Oxford University Press; 2006.
doi:10.1016/j.gaitpost.2009.07.053 Lower limb muscle activation patterns during gait: Normative data for children V. Agostini 1,∗ , A. Nascimbeni 2 , A. Gaffuri 2 , P. Caffaratto 1 , M.G. Benedetti 3 , M. Knaflitz 1
Imazio 2 , J.P.
1
Dipartimento di Elettronica, Politecnico di Torino, Torino, Italy S.C. Recupero e Rieducazione Funzionale, S. Croce Hospital, ASL 8, Moncalieri (TO), Italy 3 Laboratorio di Analisi del Movimento, Istituto Ortopedico Rizzoli, Università di Bologna, Italy 2
Introduction: Gait analysis is widely used in clinics to study walking abnormalities for surgery planning, definition of rehabilitation protocols, and objective evaluation of the clinical outcome [1]. Surface EMG is of paramount importance, since it is the only method that allows to study the muscle activity non invasively and to observe the timing of muscle activation [2]. However, at this time EMG normative data are incomplete and based on the observation of a relatively small number of strides for each subject. The aim of this study is to present a normative data set of muscle activation patterns obtained considering a very large number of strides on a population of school-age children. Methods: We analyzed a population of 100 children (51 males, 49 females) aged 6 to11. Signals were acquired by means of a multichannel recording system for statistical gait analysis (Step32, DemItalia, Italy). Each subject was instrumented with footswitches, knee goniometers, and surface EMG probes placed over Tibialis Anterior (TA), Gastrocnemius Lateralis (GL), Vastus Medialis (VM), Rectus Femoris (RF) and Biceps Femoris (BF) muscles of both lower limbs. Children were asked to walk back and forth over a 10m path, at their natural pace, for 2.5 min. More than 120 consecutive strides have been analyzed for each child. Results: The most recurrent pattern of activation of TA (48% of strides) is characterized by: (a) a first activation during the heel rocker up to 10% of Gait Cycle (GC); (b) a second activation starting before toe off (49% GC) up to 65% GC; c) a third activation starting at 79% GC up to the next initial contact. Other two modalities, consisting of two and four activation intervals, are observed in 23% and 23% of strides, respectively. The most recurrent pattern of activation of GL (34% of strides) is characterized by: (a) a first activation starting after heel rocker end (7% GC) and stopping at heel off (37% GC); (b) a second activation between initial and midswing phases (from 67% to 80% GC). Other two frequent modalities consist of three and one activation intervals, respectively, and are observed in 29% and 27% of strides. VM shows, in the most recurrent pattern (57% of strides), two activation intervals: (a) at heel strike up to midstance (20% GC);
Abstracts / Gait & Posture 30S (2009) S26–S74
Figs. 2 and 3.
Results: Fig. 1 shows an example of how LPF and EMD work on the acceleration signals in the presence of tremor: power spectral density of the raw signal shows high tremor peaks that LPF does not suppress completely. Conversely, EMD-filtered signal does not show significant components after the cutoff frequency. In Figs. 2 and 3 two representative parameters of the set are presented: jerk (a smoothness parameter) and centroidal frequency, both computed in AP during DT trials. Medians of EMD distributions are usually lower than LPF due to the bandwidth reduction. EMD dispersion is slightly decreased, compared to LPF, in control and PD trials without tremor; on the other hand dispersion in PD trials with tremor is substantially cut down by EMD. Discussion: LPF higher dispersion in tremorous trials is caused by the high-frequency components that are not adequately suppressed by the filter. Similar results can be seen for other frequency parameters, while mean acceleration module and rms acceleration module, keep the same distribution with some exceptions in ML direction where the tremor contribution is higher. Difference between LPF and EMD grows in DT condition compared to ST because PD subjects usually exhibit a higher level of tremor performing the cognitive task. These results suggest the choice of EMD filtering instead of LPF in order to have a better tremor-free estimate of posturographic parameters. References [1] G.Rilling et al., IEEE-EURASIP NSIP, Grado, Italy, 2003. [2] de Lima ER, et al. Med Biol Eng Comput 2006;44(7):569–82. [3] Chiari L, et al. Clin Biomech 2002;17(9-10):666–77.
doi:10.1016/j.gaitpost.2009.07.051 Tremor detection and tracking through sEMG analysis S. Conforto ∗ , C. De Marchis, G. Severini, T. D’Alessio Department of Applied Electronics, University Roma TRE, Rome, Italy Introduction: Tremor is a common movement disorder often connected to a neuromuscular disease (i.e. Parkinson, multiple sclerosis) whose incidence is increasing with ageing. Typical treatments, based on drugs, surgery, and deep brain stimulation, are ineffective in almost the 25% of patients thus stressing the need for solutions to reduce tremor. Up to now motion sensors [1] have been used to detect tremor aiming at its mechanical suppression. However, since methods based on inertial data cannot be easily
applied in a real-time framework because of the delay in the control chain, recently the research is trying to integrate inertial and electrophysiological signals such as surface EMG. sEMG seems adequate to detect the movement before its mechanical actuation. Several linear techniques have been used to extract muscular activation timing from sEMG. Unfortunately, they are not always suitable for tremor and often they cannot be implemented in real-time as needed in the context of the active suppression of tremor through neuroprosthesis [2]. The present study deals with the detection and tracking of tremor and its separation from voluntary movement through the use of sEMG signals. Methods: The proposed technique is based on the hypothesis that a tremor burst in a sEMG signal can be modeled as an energy distribution over a short time interval [3]. From a mathematical point of view this distribution can be described by a second order moment function (ti is the center of the window W sliding all over |x(t)| that is the rectified t +W/2 sEMG signal) t +W/2 SOMF(W, ti ) = ( ti =t −W/2 |x(tn )|(tn − ti )2 /( ti =t −W/2 |x(tn )|). The n
i
n
i
function depends on the window dimension: W should contain just one muscular burst at once for proper results. When the repetition frequency ft of the bursts is known the optimal dimension equals W = fc /ft , being fc the sampling rate. The function oscillates around W2 /12 with local minima allocated in the centre of the muscular bursts. Since it has been demonstrated theoretically that both in the onset and the offset of the muscular activation the SOMF equals to W2 /12, this value represents an automatic threshold for the detection of muscular activation. The need of knowing in advance the tremor frequency ft constitutes a drawback of the approach in [3], that we have overcome by considering that (i) tremor frequencies are in the range 3–12 Hz, (ii) SOMF calculated with W = fc /ft detects correctly tremor bursts in a range ±2 Hz around ft . In this way the actual ft , to be used for the window dimensioning, is given by a pre-estimation phase where the detection is made by averaging different SOMFs (calculated over windows covering all the tremor frequencies range). The amplitude of the tremor oscillation can be assessed by estimating the SNR that depends both on SOMF values and burst duration. Results: The main information the improved algorithm can provide are related to: (i) the onset of tremor, (ii) the frequency of tremor, (iii) the detection of muscular activation, (iv) the amplitude estimation. The algorithm has been tested on tremor sEMG signals simulated by amplitude modulating white noise series (SNR range 6–30 dB, tremor frequency range 4-11 Hz). Preliminary results show detection performance (onset and offset of the tremor) with bias lower than 9 ms and standard deviation lower than 12 ms. For SNR lower than 14 dB and for tremor frequency greater than 8 Hz, the algorithm overestimates the SNR, and then the amplitude of tremor, while increasing SNR the bias of the estimation is always lower than 2 dB. Since the algorithm has a low computational cost it could be easily implemented in real time. Discussion: SOMF is a valuable and low cost approach for a nonlinear analysis of sEMG signal. The theoretical proofs guarantee the extraction of the basic parameters of tremor by a simple and automatic thresholding operation (the threshold is objective and dependent on the analysis window dimension that can be adaptively estimated). The performance of the approach in terms of estimation consistency are comparable with the techniques widely used in the literature. Tremor characteristics are properly extracted by sEMG signals without being affected by voluntary movements. The results, even if preliminary, are encouraging and promising for the field of assistive technology for tremor control and for the studies that are in evolution such as the one presented in the European Project TREMOR (FP7 action ICT-2007.7.2 “Accessible and Inclusive ICT”, grant number ICT-2007-224051).
Abstracts / Gait & Posture 30S (2009) S26–S74
References [1] Riviere C, Thakor N. IEEE Eng Med Biol 1996;15(3):29–36. [2] Prochazka A, Gillard D, Bennett DJ. J Neurophysiol 1997;77:3237–51. [3] Journée HL, Postma AA, Sunc M, Staal MJ. Med Eng Phys 2008;30:75–83.
doi:10.1016/j.gaitpost.2009.07.052 Motor task manager and upper limb reaching task: Validation of movement analysis M.B. Warner 1 , A. Pittaluga 2,∗
Novellino 2 , M.J.
Stokes 1 , A.
Astill 1 , E.
1
School of Health Sciences, University of Southampton, UK 2 ett S.r.l., Genova, Italy
Introduction: Motor dysfunction as a result of neurological disorders, such as Parkinson’s disease and Multiple Sclerosis, is evident when performing goal orientated reaching tasks [1,2]. Standardising movement and reaching tasks are difficult however, due to the large number of degrees of freedom in the upper limb. The motor tasks manager (MTM) (ett; Genoa, Italy) has been developed to provide clinicians and researchers with a standardised, objective reaching task. Before the system can be used in a clinical environment the validity of measurements provided by the MTM must be assessed Aim: To compare MTM measurements to an established and valid motion tracking system. Methods: Participants: Nine healthy participants ages 24–55 years (mean = 31.4, SD ± 9.84) performed a goal orientated reaching task prescribed by the MTM software. Reaching task: Participants were seated at a table and moved a computer mouse horizontally across a digitising tablet on the table surface (Fig. 1a). The task involved moving a cursor on a computer screen via the digitising tablet and mouse to 8 targets presented radially around a centre start point (Fig. 1b). Each target was presented sequentially, starting at the 3 o’clock position moving anticlockwise, at intervals of 1.8 s. Motion analysis: • MTM: The MTM recorded the 2-dimensional movements of the mouse across the digitising tablet at 200 Hz. • Vicon: Three dimensional wrist movement was captured using a 6 camera Vicon 460 motion capture system (Vicon; Oxford, UK) at a sampling frequency of 200 Hz. Markers were placed on the ulnar and radial styloids and the wrist local coordinate system was defined according to the International of Society Biomechanics guidelines [3] (Fig. 2). Path length (starting point to reversal point) and peak velocity were calculated from the cursor movements of the MTM and wrist movements from the Vicon simultaneously during the MTM reaching task. Results: Path length showed good agreement between MTM and Vicon measurements with both motion tracking methods providing path lengths that were close to the expected path length of 10 cm (distance from centre start point to target). Peak velocity showed a difference between MTM and Vicon tracking methods. Typically the wrist is used for the calculation of reaching task velocity [4]. The position of the MTM mouse, the point where the MTM records movement of the arm, however is positioned beneath the hand. The offset distance between the MTM mouse and ulnar styloid (origin of the wrist) would account for the difference in velocity, particularly if the participant followed a non-linear trajectory towards the target. The MTM mouse would follow an
S57
increased radius, compared to the wrist, and result in an increased velocity. Discussion: The MTM provides valid measurements for path length and records a higher value for peak velocity when compared to that measured by a standard measurement protocol. Therefore caution will be needed when comparing MTM results for velocity to those from other motion tracking devices. Further investigations to better understand the recorded difference are needed as well.
References [1] [2] [3] [4]
Ghilardi MF et al., Brain Research, 876, 112–123. Longstaff MG et al., Human Movement Science, 25, 474–491. Wu G et al., Journal of Biomechanics. 38: 981–992. Jones LA, et al. Human hand function. Oxford University Press; 2006.
doi:10.1016/j.gaitpost.2009.07.053 Lower limb muscle activation patterns during gait: Normative data for children V. Agostini 1,∗ , A. Nascimbeni 2 , A. Gaffuri 2 , P. Caffaratto 1 , M.G. Benedetti 3 , M. Knaflitz 1
Imazio 2 , J.P.
1
Dipartimento di Elettronica, Politecnico di Torino, Torino, Italy S.C. Recupero e Rieducazione Funzionale, S. Croce Hospital, ASL 8, Moncalieri (TO), Italy 3 Laboratorio di Analisi del Movimento, Istituto Ortopedico Rizzoli, Università di Bologna, Italy 2
Introduction: Gait analysis is widely used in clinics to study walking abnormalities for surgery planning, definition of rehabilitation protocols, and objective evaluation of the clinical outcome [1]. Surface EMG is of paramount importance, since it is the only method that allows to study the muscle activity non invasively and to observe the timing of muscle activation [2]. However, at this time EMG normative data are incomplete and based on the observation of a relatively small number of strides for each subject. The aim of this study is to present a normative data set of muscle activation patterns obtained considering a very large number of strides on a population of school-age children. Methods: We analyzed a population of 100 children (51 males, 49 females) aged 6 to11. Signals were acquired by means of a multichannel recording system for statistical gait analysis (Step32, DemItalia, Italy). Each subject was instrumented with footswitches, knee goniometers, and surface EMG probes placed over Tibialis Anterior (TA), Gastrocnemius Lateralis (GL), Vastus Medialis (VM), Rectus Femoris (RF) and Biceps Femoris (BF) muscles of both lower limbs. Children were asked to walk back and forth over a 10m path, at their natural pace, for 2.5 min. More than 120 consecutive strides have been analyzed for each child. Results: The most recurrent pattern of activation of TA (48% of strides) is characterized by: (a) a first activation during the heel rocker up to 10% of Gait Cycle (GC); (b) a second activation starting before toe off (49% GC) up to 65% GC; c) a third activation starting at 79% GC up to the next initial contact. Other two modalities, consisting of two and four activation intervals, are observed in 23% and 23% of strides, respectively. The most recurrent pattern of activation of GL (34% of strides) is characterized by: (a) a first activation starting after heel rocker end (7% GC) and stopping at heel off (37% GC); (b) a second activation between initial and midswing phases (from 67% to 80% GC). Other two frequent modalities consist of three and one activation intervals, respectively, and are observed in 29% and 27% of strides. VM shows, in the most recurrent pattern (57% of strides), two activation intervals: (a) at heel strike up to midstance (20% GC);