| 1. |
- Bentes, João, 1988-, et al.
(författare)
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Novel System Architecture for Online Gait Analysis
- 2017
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Konferensbidrag (refereegranskat)abstract
- Although wearable devices can be used to perform continuous gait analysis in daily life, existing platforms only support short-term analysis in quasi-controlled environments. This paper proposes a novel system architecture that is designed for long-term, online gait analysis in free-living environments. Various aspects related to the feasibility and scalability of the proposed system are presented.
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| 2. |
- Byttner, Stefan, 1975-, et al.
(författare)
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An ion current algorithm for fast determination of high combustion variability
- 2004
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Ingår i: SAE Technical Paper Series. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191.
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Konferensbidrag (refereegranskat)abstract
- It is desirable for an engine control system to maintain a stable combustion. A high combustion variability (typically measured by the relative variations in produced work, COV(IMEP)) can indicate the use of too much EGR or a too lean air-fuel mixture, which results in less engine efficiency(in terms of fuel and emissions) and reduced driveability. The coefficient of variation (COV) of the ion current integral has previously been shown in several papers to be correlated to the coefficient of variation of IMEP for various disturbances (e.g. AFR, EGR and fuel timing). This paper presents a cycle-to-cycle ion current based method of estimating the approximate category of IMEP (either normal burn, slow burn, partial burn or misfire) for the case of lean air-fuel ratio. The rate of appearance of the partial burn and misfire categories is then shown to be well correlated with the onset of high combustion variability(high COV(IMEP)). It is demonstrated that the detection of these categories can result in faster determination(prediction) of high variability compared to only using the COV(Ion integral). Copyright © 2004 SAE International.
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| 3. |
- Byttner, Stefan, 1975-, et al.
(författare)
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Estimation of combustion variability using in-cylinder ionization measurements
- 2001
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the use of the ionization current to estimate the Coefficient of Variation for the Indicated Mean Effective Pressure, COV(IMEP), which is a common variable for combustion stability in a spark-ignited engine. Stable combustion in this definition implies that the variance of the produced work, measured over a number of consecutive combustion cycles, is small compared to the mean of the produced work. The COV(IMEP) is varied experimentally either by increasing EGR flow or by changing the air-fuel ratio, in both a laboratory setting (engine in dynamometer) and in an on-road setting. The experiments show a positive correlation between COV(Ion integral), the Coefficient of Variation for the integrated Ion Current, and COV(IMEP), when measured under low load on an engine in a dynamometer, but not under high load conditions. On-road experiments show a positive correlation, but only in the EGR and the lean burn case. An approach based on individual cycle classification for real-time estimation of combustion stability is discussed. © Copyright 2001 Society of Automotive Engineers, Inc.
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| 4. |
- Byttner, Stefan, 1975-, et al.
(författare)
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Strategies for handling the fuel additive problem in neural network based ion current interpretation
- 2001
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Ingår i: SAE Technical Paper Series. - Warrendale, PA : Society of Automotive Engineers. - 0148-7191.
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Konferensbidrag (refereegranskat)abstract
- With the introduction of unleaded gasoline, special fuel agents have appeared on the market for lubricating and cleaning the valve seats. These fuel agents often contain alkali metals that have a significant impact on the ion current signal, thus affecting strategies that use the ion current for engine control and diagnosis, e.g., for estimating the location of the pressure peak. This paper introduces a method for making neural network algorithms robust to expected disturbances in the input signal and demonstrates how well this method applies to the case of disturbances to the ion current signal due to fuel additives containing sodium. The performance of the neural estimators is compared to a Gaussian fit algorithm, which they outperform. It is also shown that using a fuel additive significantly improves the estimation of the location of the pressure peak. © 2001 Society of Automotive Engineers, Inc.
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| 5. |
- Jonsson, Magnus, 1969-, et al.
(författare)
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Vision-based low-level navigation using a feed-forward neural network
- 1997
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Ingår i: Proc. International Workshop on Mechatronical Computer Systems for Perception and Action (MCPA'97), Pisa, Italy, Feb. 10-12. ; , s. 105-111
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Konferensbidrag (refereegranskat)abstract
- In this paper we propose a simple method for low-level navigation for autonomous mobile robots, employing an artificial neural network. Both corridor following and obstacle avoidance in indoor environments are managed by the same network. Raw grayscale images of size 32 x 23 pixels are processed one at a time by a feed-forward neural network. The output signals from the network directly control the motor control system of the robot. The feed-forward network is trained using the RPROP algorithm. Experiments in both familiar and unfamiliar environments are reported.
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| 6. |
- Khandelwal, Siddhartha, 1987-, et al.
(författare)
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Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications
- 2014
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Ingår i: 13th International Symposium on 3D Analysis of Human Movement. - 9782880748562 ; , s. 151-154
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Konferensbidrag (refereegranskat)abstract
- Detecting gait events is the key to many gait analysis applications which would immensely benefit if the analysis could be carried out using wearable sensors in uncontrolled outdoor environments, enabling continuous monitoring and long-term analysis. This would allow exploring new frontiers in gait analysis by facilitating the availability of more data and empower individuals, especially patients, to avail the benefits of gait analysis in their everyday lives. Previous gait event detection algorithms impose many restrictions as they have been developed from data collected incontrolled, indoor environments. This paper proposes a robust algorithm that utilizes a priori knowledge of gait in conjunction with continuous wavelet transform analysis, to accurately identify heel strike and toe off, from noisy accelerometer signals collected during indoor and outdoor walking. The accuracy of the algorithm is evaluated by using footswitches that are considered as ground truth and the results are compared with another recently published algorithm.
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| 7. |
- Khandelwal, Siddhartha, 1987-, et al.
(författare)
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Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database
- 2017
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Ingår i: Gait & Posture. - Amsterdam : Elsevier. - 0966-6362 .- 1879-2219. ; 51, s. 84-90
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Tidskriftsartikel (refereegranskat)abstract
- Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings.This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios. © 2016 Elsevier B.V.
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| 8. |
- Khandelwal, Siddhartha, 1987-, et al.
(författare)
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Gait Event Detection in Real-World Environment for Long-Term Applications : Incorporating Domain Knowledge into Time-Frequency Analysis
- 2016
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - Piscataway, NJ : IEEE Press. - 1534-4320 .- 1558-0210. ; 24:12, s. 1363-1372
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Tidskriftsartikel (refereegranskat)abstract
- Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments. © Copyright 2016 IEEE
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| 9. |
- Khandelwal, Siddhartha, 1987-
(författare)
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Gait Event Detection in the Real World
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Healthy gait requires a balance between various neuro-physiological systems and is considered an important indicator of a subject's physical and cognitive health status. As such, health-related applications would immensely benefit by performing long-term or continuous monitoring of subjects' gait in their natural environment and everyday lives. In contrast to stationary sensors such as motion capture systems and force plates, inertial sensors provide a good alternative for such gait analysis applications as they are miniature, cheap, mobile and can be easily integrated into wearable systems.This thesis focuses on improving overall gait analysis using inertial sensors by providing a methodology for detecting gait events in real-world settings. Although the experimental protocols for such analysis have been restricted to only highly-controlled lab-like indoor settings; this thesis presents a new gait database that consists of data from gait activities carried out in both, indoor and outdoor environments. The thesis shows how domain knowledge about gait could be formulated and utilized to develop methods that are robust and can tackle real-world challenges. It also shows how the proposed approach can be generalized to estimate gait events from multiple body locations. Another aspect of this thesis is to demonstrate that the traditionally used temporal error metrics are not enough for presenting the overall performance of gait event detection methods. The thesis introduces how non-parametric tests can be used to complement them and provide a better overview.The results of comparing the proposed methodology to state-of-the-art methods showed that the approach of incorporating domain knowledge into the time-frequency analysis of the signal was robust across different real-world scenarios and outperformed other methods, especially for the scenario involving variable gait speeds in outdoor settings. The methodology was also benchmarked on publicly available gait databases yielding good performance for estimating events from different body locations. To conclude, this thesis presents a road map for the development of gait analysis systems in real-world settings.
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| 10. |
- Khandelwal, Siddhartha, 1987-, et al.
(författare)
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Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis
- 2014
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Ingår i: BIOSIGNALS 2014. - [S.l.] : SciTePress. - 9789897580116 ; , s. 197-204
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Konferensbidrag (refereegranskat)abstract
- Many gait analysis applications involve long-term or continuous monitoring which require gait measurements to be taken outdoors. Wearable inertial sensors like accelerometers have become popular for such applications as they are miniature, low-powered and inexpensive but with the drawback that they are prone to noise and require robust algorithms for precise identification of gait events. However, most gait event detection algorithms have been developed by simulating physical world environments inside controlled laboratories. In this paper, we propose a novel algorithm that robustly and efficiently identifies gait events from accelerometer signals collected during both, indoor and outdoor walking of healthy subjects. The proposed method makes adept use of prior knowledge of walking gait characteristics, referred to as expert knowledge, in conjunction with continuous wavelet transform analysis to detect gait events of heel strike and toe off. It was observed that in comparison to indoor, the outdoor walking acceleration signals were of poorer quality and highly corrupted with noise. The proposed algorithm presents an automated way to effectively analyze such noisy signals in order to identify gait events.
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