Extraction of user's navigation commands from upper body force interaction in walker assisted gait
© Neto et al; licensee BioMed Central Ltd. 2010
Received: 14 April 2010
Accepted: 5 August 2010
Published: 5 August 2010
The advances in technology make possible the incorporation of sensors and actuators in rollators, building safer robots and extending the use of walkers to a more diverse population. This paper presents a new method for the extraction of navigation related components from upper-body force interaction data in walker assisted gait. A filtering architecture is designed to cancel: (i) the high-frequency noise caused by vibrations on the walker's structure due to irregularities on the terrain or walker's wheels and (ii) the cadence related force components caused by user's trunk oscillations during gait. As a result, a third component related to user's navigation commands is distinguished.
For the cancelation of high-frequency noise, a Benedict-Bordner g-h filter was designed presenting very low values for Kinematic Tracking Error ((2.035 ± 0.358)·10-2 kgf) and delay ((1.897 ± 0.3697)·101 ms). A Fourier Linear Combiner filtering architecture was implemented for the adaptive attenuation of about 80% of the cadence related components' energy from force data. This was done without compromising the information contained in the frequencies close to such notch filters.
The presented methodology offers an effective cancelation of the undesired components from force data, allowing the system to extract in real-time voluntary user's navigation commands. Based on this real-time identification of voluntary user's commands, a classical approach to the control architecture of the robotic walker is being developed, in order to obtain stable and safe user assisted locomotion.
Standard or four-legged wakers are useful for patients with poor balance, , or for those that require some level of partial body weight support (PBWS), at the cost of compromising gait patterns and posture during gait. Upper body strength and good motor coordination are demanded for lifting up and placing forward the device during gait, .
Rollators or four-wheeled walkers offer more natural gait patterns but lack in stability. If users should put much weight on the device, it may roll away, resulting in a fall. In that context, rollators should be used by patients that require minimal weight bearing, such as individuals with mild to moderate Parkinson's disease or ataxia, .
The advances in technology make possible the incorporation of sensors and actuators in such devices, bringing to these devices new characteristics: improved the therapies based on walkers by means of assist-as-needed intervention and improved device's reliability. These new characteristics extended the use of walkers to a more diverse population. (Robotic, advanced or smart)-walkers are normally three/four-wheeled devices in which locomotion is controlled by motors, offering, at the same time, natural gait patterns, lateral stability and the possibility of PBWS. Sensors aimed at extracting user or environment conditions provide safe and efficient control. Some examples of the most significant smart walkers in the literature are found in [6–11]. A review regarding such devices along with a functional classification was presented in .
In the framework of Simbiosis Project, a robotic walker equipped with a multimodal user-machine interface was developed, . This work presents a new method for the extraction of user's navigation commands from upper-body force interaction in walker assisted gait. After a previous analysis of the force sensor data measured in walker's handles, the main components were identified. First, a high-frequency component originated from the vibrations introduced by the wheels/floor irregularities was found. These components can be attenuated by improving the device's structure. Nevertheless, in outdoors the pavement usually presents imperfections. This requires the development of efficient techniques to remove these components from force data.
Second, a component related to user's trunk oscillations, and consequently to user's gait, is observed. In previous works, , such component was characterized and continuously monitored in order to infer gait parameters from force data. Nevertheless, in this work the focus is on the third component related to user's navigation commands. It is fundamental to infer such commands from the interaction with the robotic walker for an efficient control of the device during assisted gait.
This paper presents a filtering architecture and its validation for obtaining user's navigation commands. Section 2 includes a brief presentation of the Simbiosis walker, the force measurement configuration and, most importantly, a discussion regarding the filtering designed to extract the components related to user's navigation commands. In section 3, the experimental results are presented along with the corresponding discussion. Finally, section 4 presents the conclusions and future work.
In addition, during the moments in which the subject is walking (blue highlighted area), slower oscillations are also observed in all axis of force data. In previous works, the authors demonstrated that this oscillatory component is specially observed in the vertical direction of the force data, and is a result of the lateral displacements of user's trunk, . Such oscillations are translated into forearm reactions as the user is supported by the walker. Movements of user's trunk and, consequently, user's center of gravity (CoG) are highly correlated with gait phases, . In , the authors proposed a methodology for the extraction of gait parameters, such as heel-strike, toe-off and cadence, from this force component.
Finally, transient events related to user's navigation commands are also found within the force sensor data. The main objective for the installation of force sensors in walker's handles was the identification and characterization of user's guidance intentions. As an example, at the beginning of the blue area in Figure 2, a high amplitude peak identifying the initial pushing force to move the device is observed. More information related to guidance commands is within this force signal, but they can not be easily identified without its proper extraction from the two previously commented components. Next section introduces the new methodology for the extraction of the components related to user's guidance intentions.
For the validation of the filtering methodology following presented, five healthy subjects were asked to walk with the device in a 40 m track prepared for the experiments. The track was placed in a indoors installation and included a 90 degrees curve at the center. The five subjects were asked to walk, at preferred speed, three times in each direction, resulting in a total of 30 repetitions. It took from 50 to 70 seconds for the subjects to complete the track each time. During the experiments, force data was acquired at 1 KHz and stored for the analysis presented in the following sections. Informed consent was obtained from the patients that participated in this study.
The subjects recruited for the proposed experiments presented no history of any dysfunction on either upper or lower limbs. At this point in the study, reference signals and validation of the method are the main objectives. Individuals with pathological gait will be addressed in the future.
On the other, the lower branch is constructed to online estimate the component caused by user's trunk oscillations and, therefore, highly correlated with user's cadence. This last component is, then, subtracted from the force data filtered by the first block. As it can be seen, in addition to the force signals, cadence is also an input for this filter. The idea is to selectively and adaptively filter the force data without compromising the amplitude of components which frequencies are close to gait cadence as they can contain relevant information regarding user's intents.
Design of high-frequency noise cancelation filter
The technique presented in this section relies on the high-frequency of the force components related to the vibrations of the walker's structure. Classical low-pass filters, such as Butterworth, Chebyshev, among others, can be used for the cancelation of high-frequency components of the acquired force signals, nevertheless, such approach would also introduce an important phase shift between input and outputs signals causing a temporal delay on the filtered signal. Such situation is undesirable in real-time applications once delay affect the cognitive interaction between the walker and the user.
Equations 1 and 2 are designated as update, tracking, or filtering equations. They estimate the current position, x k, k , and velocity, , of the variable based on previous predicted position, x k, k-1, and velocity, , taking the current measurement y k to account. Confidence on measures is weighted by gains g k and h k . Equations 3 and 4 are called prediction equations as they provide a prediction of future position and velocity, x k+1,k , , based on first order dynamic model of the variable. As g-h trackers consider a constant velocity model, predicted velocity is equal to the current one, . The assumption of constant speed is reasonable considering that human movements are slow, presenting small accelerations, , and that the data is sampled at high rates (in this study, f sampling = 1kHz).
G-h filters are affected by two error sources, : (i) the lag, dynamic, bias or systematic error, which are related to the constant velocity assumption, and (ii) the measurement error, which is inherent to the sensor and measurement process. Typically, the smaller g k and h k are, the larger is the dynamic error and the smaller are the measurement errors, . In designing a g-h tracking filter there is a degree of freedom in choice of the relative magnitude of the measurement and dynamic errors.
Where, is the mean square of errors of the filtered signal and σ2 is the variance both related to a reference signal obtained through offline filtering the signal with the algorithm known as zero-phase forward and reverse digital filtering, . This last filtering algorithm is non-causal once the signal is filtered both in forward and reverse directions in time and it can be only used in offline applications. Nevertheless, it offers an optimal reference signal for the proper selection of filter's coefficients, considering that the filter yields precisely zero-phase distortion.
Selection of best filter coefficients based on the KTE.
(2.194 ± 0.1455)·10-1
(1.583 ± 0.1462)·101
(2.817 ± 0.1795)·10-2
F Y left
(2.131 ± 0.0893)·10-1
(1.517 ± 0.1951)·101
(2.900 ± 0.2828)·10-2
(1.396 ± 0.1569)·10-1
(1.933 ± 0.3902)·101
(2.517 ± 0.3727)·10-2
(1.333 ± 0.1414)·10-1
(1.850 ± 0.4113)·101
(2.633 ± 0.4758)·10-2
(2.203 ± 0.4284)·10-1
(1.900 ± 0.4397)·101
(2.425 ± 0.3521)·10-2
(2.291 ± 0.3513)·10-1
(1.933 ± 0.2134)·101
(2.542 ± 0.2070)·10-2
(2.105 ± 0.2947)·10-1
(1.967 ± 0.1972)·101
(2.223 ± 0.1863)·10-2
F Y left
(2.019 ± 0.2605)·10-1
(1.967 ± 1.5986)·101
(2.292 ± 0.1170)·10-2
(2.183 ± 0.2209)·10-1
(2.383 ± 0.2544)·101
(2.041 ± 0.1538)·10-2
(2.284 ± 0.2446)·10-1
(2.100 ± 0.3215)·101
(2.229 ± 0.2634)·10-2
(2.014 ± 0.4194)·10-1
(1.9133 ± 0.3721)·101
(2.469 ± 0.3781)·10-2
(2.124 ± 0.1447)·10-1
(2.050 ± 0.2432)·101
(9.857 ± 0.01374)·10-1
F Y left
(2.056 ± 0.0896)·10-1
(1.967 ± 0.2494)·101
(9.850 ± 0.01528)·10-1
(1.321 ± 0.1437)·10-1
(2.533 ± 0.4988)·101
(9.877 ± 0.02054)·10-1
F Y left
(1.262 ± 0.1311)·10-1
(2.217 ± 0.3891)·101
(9.862 ± 0.02267)·10-1
(2.108 ± 0.3627)·10-1
(2.533 ± 0.6600)·101
(9.882 ± 0.02340)·10-1
F Y left
(2.174 ± 0.3218)·10-1
(2.617 ± 0.3184)·101
(9.878 ± 0.01344)·10-1
(2.035 ± 0.2687)·10-1
(2.567 ± 0.3815)·101
(9.890 ± 0.01633)·10-1
F Y left
(1.947 ± 0.2533)·10-1
(2.433 ± 0.1795)·101
(9.883 ± 0.00745)·10-1
(2.094 ± 0.2037)·10-1
(2.967 ± 0.3543)·101
(9.900 ± 0.01155)·10-1
F Y left
(2.177 ± 0.2495)·10-1
(2.783 ± 0.3288)·101
(9.899 ± 0.01344)·10-1
(1.923 ± 0.4002)·10-1
(2.467 ± 0.4847)·101
(9.877 ± 0.0225)·10-1
Estimation of force component related to gait cadence
Where y k is the input signal. The adaptive weight vector, W k , generates a linear combination of the harmonic orthogonal sinusoidal components of the reference input vector, X k . As previously described, M is the number of the harmonics used and, finally, μ represents the amplitude adaptation gain used for the LMS recursion.
As mentioned before, the algorithm needs a frequency input for the correct estimation of the gait related force component. On the one hand, such information can be offered by an external system, such as a podometer (or any step counter). In this context, the authors proposed in  an ultrasonic subsystem that offer continuously the distance between each user's feet and the walker. From that information, cadence can be easily extracted and used for the FLC algorithm. The main disadvantage of this approach is that the user has to wear sensors on each feet compromising the usability of the device.
On the other hand, the author's also demonstrated in  that the vertical components of the force signals can be used for continuous estimation of gait cadence using the Weighted-Frequency Fourier Linear Combiner (WFLC). The WFLC is an extension of the FLC noise canceler presented before and also tracks frequency of the input signal based on a LMS recursion. Therefore, the WFLC adapts in real-time its amplitude, frequency and phase, .
As the WLFC is designed to adapt to the dominant-frequency component in a signal , it is important to perform a previous stage of band-pass filtering (compatible with gait cadence frequencies) for the correct performance of the WFLC. Although this filtering stage can cause undesirable time delay in the force signals, instantaneous temporal changes in gait cadence (WFLC's frequency output) are minimal.
Therefore, an external branch of cadence estimation based force measurement, and the WFLC algorithm showed to be very useful in the application presented in this paper.
During the preliminary experiments, the method for filtering the force sensor data was observed to be very effective for canceling the cadence-related components in symmetrical gait, in which the user applies approximately the same amount of body weight to both supporting platforms. Nevertheless, when planning the filter design to help people with pathological gait, it is interesting to include the possibility for the cancelation of components related to asymmetrical supporting forces. When such situation occurs, it was experimentally observed that the oscillatory component presents more influence due to the cadence of one foot.
In previous works, the authors presented a methodology for tuning the WFLC parameters for the online estimation of cadence from force data, . This methodology consists in adjusting five parameters. Three of them do not require tuning: the number of harmonics of the model, M, which is fixed to 1, the instantaneous frequency at initialization, which is automatically set as the lower cut-off frequency of the band-pass filter (0.5 Hz in this work) and the bias weight to compensate for low frequency drifts, which is set to zero in this application. Finally, the amplitude and frequency update weights are adjusted in a manner that the frequency output of the WFLC adapts as fast as possible to the dominant-frequency component of the input signals.
Since the WFLC tuning was previously solved, authors start from the supposition that continuous cadence is a known parameter. Only FLC tuning is required. As the number of the harmonics (M) of the FLC is set to 2, only one parameter needs to be adjusted: the amplitude adaptation gain, μ, used for the LMS recursion. The selection of values for μ affects directly the convergency time and, most importantly, the bandwidth (BW) of the FLC adaptive filter, . For values of μ ≪1, the bandwidth is given by BW ≈ 2μ.
Considering that, small values of μ imply in a narrow band filter and the cancelation of a very specific frequency. Nevertheless, if the force component signal is not a perfect sinusoidal wave, the cancelation will not be effective. Opposite to the g-h filters selection, in this case, no reference signal can be obtained for the automatic/optimal selection of the FLC parameter. As in other similar works, such as , the selection of μ is usually performed empirically.
Results and Discussion
Attenuation of cadence related components by means of WFLC-FLC algorithms.
R (mean ± std. deviation)
0.8084 ± 0.0140
0.7605 ± 0.1104
0.7915 ± 0.0147
0.8045 ± 0.0173
0.7970 ± 0.0204
The authors would like to thank the Spanish National Program of R&D - DPI that supports the Simbiosis Project (DPI2005-07417). Written consent for publication was obtained from the patient or their relative. The SIMBIOSIS Project is placed in the framework of the Spanish National Program of R&D and it is approved, along with the experimental and validation procedures, by Spanish Ministry of Science and Innovation (MICINN).
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