Abstract
In this paper a novel step length model using a handheld Micro Electrical Mechanical System (MEMS) is presented. It combines the user's step frequency and height with a set of three parameters for estimating step length. The model has been developed and trained using 12 different subjects: six men and six women. For reliable estimation of the step frequency with a handheld device, the frequency content of the handheld sensor's signal is extracted by applying the Short Time Fourier Transform (STFT) independently from the step detection process. The relationship between step and hand frequencies is analyzed for different hand's motions and sensor carrying modes. For this purpose, the frequency content of synchronized signals collected with two sensors placed in the hand and on the foot of a pedestrian has been extracted. Performance of the proposed step length model is assessed with several field tests involving 10 test subjects different from the above 12. The percentages of error over the travelled distance using universal parameters and a set of parameters calibrated for each subject are compared. The fitted solutions show an error between 2.5 and 5% of the travelled distance, which is comparable with that achieved by models proposed in the literature for body fixed sensors only.
Highlights
Recent progress in Micro Electro Mechanical Systems (MEMS) technology is stimulating their use in different domains including pedestrian navigation, location based services (LBS), safety and healthcare services
For assessing the performance of the proposed model in the position domain, a second experiment was conducted in an open soccer field with different test subjects than the ones who participated to the fitting of the universal step length model
In order to assess the performance of the proposed step length model, the user’s motion mode and step events are first identified
Summary
Recent progress in Micro Electro Mechanical Systems (MEMS) technology is stimulating their use in different domains including pedestrian navigation, location based services (LBS), safety and healthcare services. The majority of existing algorithms assumes that the sensor is rigidly attached to the user’s body either on the foot, close to the Centre Of Mass (COM), e.g., along the backbone, or distributed on the leg [2,3,4,5,6] These locations are suitable for navigation purposes since the inertial force experienced by the sensor is directly linked to the gait cycle. Very few studies target the handheld sensor case and in general only the case of sensors held in the user’s phoning or texting hand is considered [13] In this context, the sensor is mainly experiencing the inertial force produced by the global motion of the user, which is similar to the body fixed case. The assessment part, performed with 10 test subjects, shows that the handheld step length model achieves comparable performances as the ones obtained in the literature but with body fixed sensors only.
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