Description:TECHNICAL FIELD
[0001] The present disclosure relates, in general, to navigation systems and more specifically, relates to a system and method for pedestrian navigation for detection of steps and step direction of a pedestrian.

BACKGROUND
[0002] Numerous navigation applications, including search and rescue, military, sports, tourism, commercial location-based services, and many more, require pedestrian tracking and positioning. Several pedestrian navigation devices are known that are light-weighted and cost-effective. There are known hand-held aids, as well as body-mounted aids e.g. devices mounted on head, shoulder, belt, shoe or foot for pedestrian navigation.
[0003] In the existing pedestrian navigation devices, a step detection process generally uses digital filters and dynamic thresholds along with a time window on acceleration measurements to decide whether an effective step has been taken. The digital filters comprise linear-shift-registers and a summing unit which smoothes the acceleration signals by putting movement average on the sensor data. Since MEMS sensors are very noisy, more registers are needed to make acceleration data smoother which in turn slows down a response time. But in case the acceleration data is not smoother, the vertical acceleration data that is used in the step detection process will have multiple peaks when a step is taken, due to which a single step gets wrongly detected as multiple steps.
[0004] Further, in the existing pedestrian navigation device that utilises dead reckoning techniques, non-forward walking motions must also be taken into account in order to arrive at the accurate position of the user, while determining the position of the user. In such scenarios, the heading shown by a magnetic sensor is the same as the heading along the direction where the user is facing. Hence, in case backward and lateral walking motions are not taken into account, errors will be introduced in the calculated position whenever the direction of walking of the user is different from the direction of facing.
[0005] A patent document US7169084 entitled “Electronic pedometer” relates to an electronic pedometer which is used by mounting it on a human body in order to electronically count the steps by a person having the electronic pedometer mounted. Fixed noise of a signal detected by an acceleration detecting portion is removed by a filter portion, a walk cycle comparing portion compares a cycle of the resultant signal with a moving average value calculated by a walk cycle calculating portion, and each cycle within a predetermined cycle range is counted as the number of steps for one step by a step number counting portion.
[0006] Another patent US7463997 entitled “Pedometer device and step detection method using an algorithm for self-adaptive computation of acceleration thresholds”. The patent document relates to a processing unit connected to the accelerometer sensor, processes an acceleration signal relating to the vertical acceleration in order to detect the occurrence of a step, and compares the acceleration signal with a first reference threshold. The processing unit modifies the first reference threshold as a function of an envelope of the amplitude of the acceleration signal.
[0007] Further, US patent document US7698097 entitled “Method for controlling a pedometer based on the use of inertial sensors and pedometer implementing the method” relates to controlling a pedometer based on the use of inertial sensors. The method for controlling a pedometer includes: generating a signal correlated to movements of a user of the pedometer, detecting steps of the user based on the signal, checking whether sequences of the detected steps satisfy pre-determined conditions of regularity, and updating a total number of valid steps if the conditions of regularity are satisfied. Step is recognized if the vertical acceleration signal shows a positive peak, higher than a positive acceleration threshold followed by a negative peak, smaller than a negative acceleration threshold and if the negative peak falls within a time-window of pre-determined amplitude and located at a pre-determined distance after the positive peak.
[0008] Another patent document US5583776 entitled “Dead reckoning navigational system using accelerometer to measure foot impacts” relates to navigational systems that utilise a radio navigational data and a dead reckoning for foot navigation. A step detection is used to determine distance by a scale factor. A sliding window of the data is maintained with an odd number of samples. New accelerometer sample is placed in the near end and the samples in the sliding window are shifted. The middle sample is compared to all the others. If it is greater than the rest, then it is said that there is a step or peak in the data else another sample is taken. The magnitude of the peak must be above a minimum threshold and time since the last peak must be greater than some minimum period.
[0009] Also, the patent document US6813582 entitled “Navigational device for personnel on foot” relates to a method for dead reckoning navigation for a person on foot. Various correlations are used to distinguish between runnings versus walking, forward steps versus backward steps, left turn versus right turn. The filtered signal from vertical accelerometer is processed to identify the instance of a footstep. The method used to detect step responds to zero crossing, a moment when the direction of acceleration reverses.
[0010] Another patent document US6826477 entitled “Pedestrian navigation method and apparatus operative in a dead reckoning mode” relates to the fields of pedestrian navigation based on dead- reckoning approach, in which the evolving position of a pedestrian is determined from within his or her frame of reference. A waveform analysis algorithm based on Fourier analysis is used to detect peaks in the vertical acceleration and is the characteristic feature involved in determining the step.
[0011] However, the aforementioned documents nowhere disclose any solution to wrong detection of a single step as multiple steps whenever multiple peaks occur in vertical acceleration signal. Therefore, there is a need in the art to provide a system for pedestrian navigation for determining the steps and steps direction of a pedestrian.

OBJECTS OF THE PRESENT DISCLOSURE
[0012] An object of the present disclosure relates, in general, to navigation systems and more specifically, relates to a system and method for pedestrian navigation for detection of steps and step direction of a pedestrian.
[0013] Another object of the present disclosure is to provide a system providing a solution to wrong detection of a single step as multiple steps due to noisy Micro-Electrochemical Systems (MEMS) sensors occurring in vertical acceleration signals.
[0014] Another object of the present disclosure is to provide a system that also considers non-forward walking motions in order to find an accurate position of the user.
[0015] Another object of the present disclosure is to provide a system that utilises vertical, forward and lateral acceleration data for accurately finding the direction of steps taken.

SUMMARY
[0016] The present disclosure relates, in general, to navigation systems and more specifically, relates to a system and method for pedestrian navigation for detection of steps and step direction of a pedestrian.
[0017] The present disclosure relates to a system for pedestrian navigation, the system comprises a pedometer mounted on a waist of a pedestrian. One or more sensors are configured in the pedometer to detect a set of acceleration data pertain to a vertical acceleration data, a forward acceleration data and a lateral acceleration data. The system comprises a processing element that is interfaced with the one or more sensors. The processor element is configured to receive, at a data collection unit, from the one or more sensors, the set of acceleration data. The processor element is further configured to detect, by a step detection unit, a number of steps of the pedestrian based on the vertical acceleration data so as to mitigate false step detections caused by multiple peaks in the vertical acceleration data. The processor element is further configured to estimate, by a stride length estimation unit, length of each step of the number of steps and calculate the total distance travelled. Further, the processor element is configured to determine, by a pedometry unit, from the detected forward acceleration data and the lateral acceleration data, direction of the number of steps of the pedestrian corresponding to the detected step. Furthermore, the processor element is configured to classify the direction of the number of steps into a front direction, a back direction, a right direction, and a left direction based on the analysis of the forward acceleration data and the lateral acceleration data. Moreover, the processor element is configured to calculate, by a position calculation unit, a current position of the pedestrian with respect to a start point, based on the detected number of steps and the direction of the number of steps and display, by a display unit, the current position of the pedestrian.
[0018] In an embodiment, the number of steps detected by the pedestrian may pertain to slow walk, normal walk, fast walk and running.
[0019] In an embodiment, the processing element may be configured to determine the number of steps of the pedestrian, where the processing element may be configured to smoothen, by a data smoothening unit, the set of acceleration data to minimize impact of noisy signals. The processing element may be further configured to dynamically adjust, by a threshold finding unit, a step detection threshold based on characteristics of the vertical acceleration data to determine whether a step has been taken. The step detection threshold is dynamically calculated as an average of a minimum value and a maximum value of a set of samples of the vertical acceleration data.
[0020] In an embodiment, the processing element may be configured to execute a decision-making unit to analyse the set of samples of the vertical acceleration data to detect occurrence of the number of steps, by counting values of the set of samples of the vertical acceleration data that are below the step detection threshold thereby addressing the issue of the multiple peaks occurring in the vertical acceleration data whenever the step is taken. Further, the processing element may be configured to determine the step taken by the pedestrian if the count of the values of the vertical acceleration data is greater than a predefined number, wherein the predefined number is set to zero once the step is detected and incremented every time the set of samples of the vertical acceleration data is found below the step detection threshold after a step detection point.
[0021] In an embodiment, the processing element may be configured to estimate length of each step of the number of steps, the processing element may be configured to utilize the stride length estimation unit to calculate the length of each step by incorporating parameters pertaining to maximum acceleration value measured within the step, minimum acceleration value for the step, average acceleration value for the step and accumulate a stride length value for each step to determine the total distance travelled.
[0022] In an embodiment, the processing element may be configured to analyse the direction of the step to distinguish whether the pedestrian is making the number of steps in the forward-backward direction or in lateral direction, the processing element may be configured to store the set of samples of the forward acceleration data and the lateral acceleration data between two consecutive steps. The processor element may be configured to find variation of the set of samples of the forward acceleration data and the lateral acceleration data from the stored data. Further, the processor element may be configured to determine that the pedestrian is making the step in the forward-backward direction, if the variation in the set of samples of the forward acceleration data is greater than the variation in the lateral acceleration data. Furthermore, the processor element may be configured to determine that the pedestrian is making the step in the lateral direction, if the variation in the set of samples of the lateral acceleration data is greater than the variation in forward acceleration data.
[0023] In an embodiment, processing element may be configured to distinguish between the left direction and the right direction, the processing element configured to determine the two consecutive trough points in the vertical acceleration data. The processor element may be configured to store the set of samples of the lateral acceleration data present between the determined trough points. The processor element may be further configured to determine that the pedestrian is making the step in the left direction if in the stored set of samples of the lateral acceleration data, a maximum value is present before a minimum value. Furthermore, the processor element may be configured to determine that a pedestrian is making the step in the right direction if in the stored set of samples of the lateral acceleration data, the minimum value is present before the maximum value.
[0024] In an embodiment, the trough point may be a last sample below the step detection threshold along falling side of the vertical acceleration data having the step detection point.
[0025] In another aspect, the present disclosure provides a method for pedestrian navigation. The method includes receiving, at a data collection unit, from the one or more sensors, the set of acceleration data pertain to vertical acceleration data, forward acceleration data and lateral acceleration data. The method includes detecting, by a step detection unit, a number of steps of the pedestrian based on the vertical acceleration data so as to mitigate false step detections caused by multiple peaks in the vertical acceleration data. Further, the method includes estimating, by a stride length estimation unit, length of each step of the number of steps and calculating the total distance travelled. Further, the method includes determining, by a pedometry unit, from the detected forward acceleration data and the lateral acceleration data, direction of the number of steps of the pedestrian corresponding to the detected step. Furthermore, the method also includes classifying the direction of the number of steps into a front direction, a back direction, a right direction, and a left direction based on the analysis of the forward acceleration data and the lateral acceleration data. Moreover, the method includes calculating, by a position calculation unit, a current position of the pedestrian with respect to a start point, based on the detected number of steps and the direction of the number of steps and displaying, by a display unit, the current position of the pedestrian.
[0026] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0028] FIG. 1 illustrates a block diagram of a system for pedestrian navigation, in accordance with an embodiment of the present disclosure.
[0029] FIG. 2 illustrates a flow diagram describing the step detection process in a method for pedestrian navigation, in accordance with an embodiment of the present disclosure.
[0030] FIG. 3 illustrates a flow diagram describing the direction of the step determination process in the method for pedestrian navigation, in accordance with an embodiment of the present disclosure.
[0031] FIG. 4 illustrates a block diagram describing a method for pedestrian navigation, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0032] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0033] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0034] The present disclosure relates, in general, to navigation systems and more specifically, relates to a system and method for pedestrian navigation for detection of steps and step direction of a pedestrian. For the estimation of the position of a pedestrian, the information related to step detection, direction of movement and step length is required. The disclosed method detects and counts the number of steps taken by a person as well as determines the direction of step using a set of acceleration data received from one or more sensors configured in a pedometer. The present disclosure provides a solution to the wrong detection of a single step as multiple steps whenever the multiple peaks occur in vertical acceleration data.
[0035] The system and method of detection and counting the number of steps taken by a person having the pedometer mounted on his/her waist by using vertical acceleration signal and also determining the direction of step using forward and lateral acceleration signals from MEMS inertial sensors. The present invention claims the solution to wrong detection of a single step as multiple steps whenever multiple peaks due to noisy MEMS sensor occur in vertical acceleration signal. The present invention also claims the scheme for determining the direction of step which classifies the step direction as front, back, right or left based on the analysis of forward and side acceleration signals.
[0036] For position estimation of a pedestrian, the information related to step detection, direction of movement, step length are required. The brief objective of the present invention is to develop a scheme that detects and counts the number of steps taken by a person as well as determines the direction of step using the acceleration data from MEMS inertial sensors. This invention claims the scheme for direction of step classification and solution to wrong detection of a single step as multiple steps whenever multiple peaks occur in vertical acceleration signal. The features unique to the present step detection scheme include counting the number of vertical acceleration samples that are below threshold in addition to a threshold finding and a time window conditions for step finding. The number of vertical acceleration samples counting condition is added to take care of multiple peaks occurring in vertical acceleration signal when a step is taken as a result of which single step will be wrongly detected as multiple steps. Although N-point, N>1 moving average is used to smooth the acceleration signals, the vertical acceleration signal used in step detection still contains multiple peaks when a step is taken because MEMS sensors are very noisy. Increasing the number of data points in the moving average will increase the memory requirement and response time would be slower.
[0037] Step detection scheme in the present disclosure recognizes slow walk, normal walk, fast walk and also running. The scheme for direction of step classification is divided into three steps: forward or lateral axis filtering based on the axis having maximum variation. Forward or backward step detection based on threshold crossing of first or last sample stored between two consecutive troughs. Left or right step detection based on the position of maximum and minimum value in the samples stored between two consecutive troughs. The heading is modified based on the direction detected accordingly. One of the key features of the present disclosure is accurate step detection for different walk motions, step direction determination and real time working.
[0038] The present disclosure relates to a system for pedestrian navigation, the system comprises a pedometer mounted on a waist of a pedestrian. One or more sensors are configured in the pedometer, to detect a set of acceleration data pertain to a vertical acceleration data, a forward acceleration data and a lateral acceleration data. The system comprises a processing element is interfaced with the one or more sensors. The processor element is configured to receive, at a data collection unit, from the one or more sensors, the set of acceleration data. The processor element is further configured to detect, by a step detection unit, a number of steps of the pedestrian based on the vertical acceleration data so as to mitigate false step detections caused by multiple peaks in the vertical acceleration data. The processor element is configured to estimate, by a stride length estimation unit, length of each step of the number of steps and calculate the total distance travelled. Further, the processor element is configured to determine, by a pedometry unit, from the detected forward acceleration data and the lateral acceleration data, direction of the number of steps of the pedestrian corresponding to the detected step. Furthermore, the processor element is configured to classify the direction of the number of steps into a front direction, a back direction, a right direction, and a left direction based on the analysis of the forward acceleration data and the lateral acceleration data. Moreover, the processor element is configured to calculate, by a position calculation unit, a current position of the pedestrian with respect to a start point, based on the detected number of steps and the direction of the number of steps and display, by a display unit, the current position of the pedestrian. The present disclosure can be described in enabling detail in the following examples, which may represent more than one embodiment of the present disclosure.
[0039] FIG. 1 illustrates a block diagram of system for pedestrian navigation, in accordance with an embodiment of the present invention.
[0040] According to the present disclosure and referring to FIG. 1, the system 100 comprises a pedometer mounted on a waist of a pedestrian. One or more sensors 102 is configured within the pedometer. The one or more sensors 102 include one or more accelerometers and one or more gyroscopes to sense acceleration along three orthogonal directions and angular motion about three orthogonal axes. The one or more sensors 102 is configured to detect a set of acceleration data pertain to vertical acceleration data, forward acceleration data and lateral acceleration data. The system includes a processing element 120 interfaced with the one or more sensors 102. The processing element 120 includes a data collection unit 104 to receive a set of acceleration data from the one or more sensors 102. The system 100 includes a data smoothening unit 106 configured to smoothen the set of acceleration data to minimize impact of noisy signals.
[0041] Further, a threshold finding unit 108 is configured in the system 100 to dynamically adjust a step detection threshold based on characteristics of the vertical acceleration data to determine whether a step has been taken, wherein the step detection threshold is dynamically calculated as an average of a minimum value and a maximum value of a set of samples of the vertical acceleration data.
[0042] Furthermore, the system 100 is configured with a step detection unit 110 to detect a number of steps of the pedestrian based on the vertical acceleration data so as to mitigate false step detections caused by multiple peaks in the vertical acceleration data. A stride length estimation unit 112 is also configured in the system 100 to estimate length of each step of the number of steps and calculate the total distance travelled by accumulating the stride length value for each step. Furthermore, the system 100 is configured with a pedometry unit 114 to determine from the detected forward acceleration data and the lateral acceleration data, direction of the number of steps of the pedestrian corresponding to the detected step and classify the direction of the number of steps into a front direction, a back direction, a right direction, and a left direction based on the analysis of the forward acceleration data and the lateral acceleration data. Moreover, a display unit 118 is also provided in the system 100 to display the current position of the pedestrian.
[0043] In an embodiment, a displacement along north may be obtained by multiplying the stride length with sine of heading of a pedestrian while the displacement along east may be obtained by multiplying the stride length with cosine of heading of the pedestrian.
[0044] In an embodiment, the number of steps detected by the pedestrian may pertain to slow walk, normal walk, fast walk and running.
[0045] In an embodiment, the one or more sensors 102 can be Micro-electromechanical system (MEMS) sensors.
[0046] In an embodiment, the processing element 120 may be configured to analyse the direction of the step to distinguish whether the pedestrian is making the number of steps in the forward-backward direction or in lateral direction, the processing element 120 may be configured to store the set of samples of the forward acceleration data and the lateral acceleration data between two consecutive steps. The processor element may be configured to find variation of the set of samples of the forward acceleration data and the lateral acceleration data from the stored data. Further, the processor element may be configured to determine that the pedestrian is making the step in the forward-backward direction, if the variation in the set of samples of the forward acceleration data is greater than the variation in the lateral acceleration data. Furthermore, the processor element may be configured to determine that the pedestrian is making the step in the lateral direction, if the variation in the set of samples of the lateral acceleration data is greater than the variation in forward acceleration data.
[0047] In an embodiment, the processing element 120 may be configured to distinguish between the left direction and the right direction, the processing element 120 may be configured to determine the two consecutive trough points in the vertical acceleration data. The processor element may be configured to store the set of samples of the lateral acceleration data present between the determined trough points. The processor element may be further configured to determine that the pedestrian is making the step in the left direction if in the stored set of samples of the lateral acceleration data, a maximum value is present before a minimum value. Furthermore, the processor element may be configured to determine that the pedestrian is making the step in the right direction if in the stored set of samples of the lateral acceleration data, the minimum value is present before the maximum value.
[0048] In an embodiment, the processing element 120 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) may be configured to fetch and execute computer-readable instructions stored in a memory of the system 100. The memory can store one or more computer-readable instructions or routines, which may be fetched from the one or more sensors 102 and executed to calculate a current position of the pedestrian with respect to a start point, based on detected number of steps and a direction of the number of step of pedestrian by using a set of acceleration data received from the one or more sensors 102. The memory can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0049] FIG. 2 illustrates the flow chart describing the step detection process, according to an embodiment of the present invention.
[0050] At step 202, the method 200 may include initializing step count.
[0051] At step 204, the method 200 include collecting a set of acceleration data pertain to a vertical acceleration data, a forward acceleration data and a lateral acceleration data, along three-orthogonal axes from the one or more MEMS sensors 102 for every 10 millisecond.
[0052] At step 206, the method 200 may include smoothing and filtering the set of acceleration data by using N-point moving average method, where N>1 and finding 30 points moving average filtered output of the set of acceleration data.
[0053] At step 208, the method 200 may include counting the number of steps taken by a pedestrian and in case step count = 0, then at step 210, the method may include accumulating filtered output of the set of acceleration data at rest and finding a rest average for each of the three orthogonal-axes. The rest average calculation may be done before the pedestrian starts taking steps.
[0054] In case, step count ? 0, proceeding to step 212. At step 212, the method 200 may include finding a maximum and a minimum value for set of samples of the vertical acceleration data. In some embodiments, the maximum and minimum value is determined for every 50 samples.
[0055] At step 214, the method may include determining a dynamic threshold value, which is the average of the minimum and the maximum value of the set of samples the vertical acceleration data. In an exemplary embodiment, the dynamic threshold value is determined for next 50 samples. The threshold value is used to decide whether the step has been taken by the pedestrian or not.
[0056] At step 216, the method 200 may include comparing current value of vertical acceleration data with the rest average and in case, the current value of vertical acceleration data is greater than the rest average, then proceeding to step 218, otherwise switch back to step 202.
[0057] At step 218, the method 200 may include checking whether the current count of value of vertical acceleration data < previous count of value of vertical acceleration data > dynamic threshold value or not. If YES, then the method 200 may include counting number of steps taken by the pedestrian again at step 220 and if NO, then proceed back to step 202. This condition is the first condition out of the pre-defined condition for the step detection.
[0058] At step 220, the method 200 may include checking for step count and then the number is set to zero once a step is detected and at step 230, incremented every time the current count of value of vertical acceleration data < previous count of value of vertical acceleration data > dynamic threshold value after the step detection point.
[0059] At step 220, in case the step is not detected, proceed to step 222.
[0060] At step 222, the method 200 may include checking for a time window condition. The time window is used to discard the invalid vibrations. The steps with intervals outside the time window of 0.2 seconds to 2.0 seconds are discarded.
[0061] At step 224, the method 200 may include counting the number of value of the set of samples the vertical acceleration data that are below the dynamic threshold value. This is a third condition of the pre-defined condition. The third condition is added to take care of multiple peaks occurring in the value of the vertical acceleration data whenever a step is taken as a result of which single step will be wrongly detected as multiple steps.
[0062] At step 226, the method 200 may further include checking that the number of value of the vertical acceleration data below the dynamic threshold value must be greater than a fixed bound. This number will be set to zero once a step is detected and incremented every time a set of sample of the vertical acceleration data is found below threshold after the step detection point, at step 228. Only the first condition out of the three conditions has to be satisfied for the first step to be detected. For subsequent steps to be detected, all the three conditions have to be satisfied.
[0063] In an embodiment, the processing element 120 may be configured to execute a decision-making unit to analyse the set of samples of the vertical acceleration data to detect occurrence of the number of steps, by counting values of the set of samples of the vertical acceleration data that are below the step detection threshold thereby addressing the issue of the multiple peaks occurring in the vertical acceleration data whenever the step is taken.
[0064] FIG. 3 shows a method 300 for analysing the direction of step which is classified as front, back, left or right step, in accordance with an embodiment of the present invention.
[0065] At step 302, the method 300 may include storing a set of samples of the forward acceleration data (afwd) and a set of samples of the lateral acceleration data (alat) between two consecutive steps after subtracting the rest average value from the forward and lateral acceleration data.
[0066] At step 304, the method 300 may include determining maximum and minimum values from the stored samples of the forward and the lateral acceleration data.
[0067] At step 306, the method 300 may also include determining absolute value of difference between maximum and minimum values along forward and lateral axis respectively, and in case the absolute along the forward axis is greater than the absolute value along the lateral axis, then the step is assumed to be in the forward or backward direction, otherwise the pedestrian is making a step in right or left direction.
[0068] In case of the forward or the backward direction determined at step 308, proceed to step 310. At step 310, the method 300 may include storing a set of samples of the forward acceleration data between two consecutive troughs after subtracting the rest average value from the vertical acceleration values. Trough is the minimum point present between two consecutive step detection points in vertical acceleration values.
[0069] At step 312, the method 300 may include accumulating the set of samples of the forward acceleration data and determining an average value.
[0070] At step 314, the method 300 may include checking whether the average value is less than a first or a last stored set of acceleration data. In case yes, the step is assumed to be in the forward direction (step 318), otherwise the step is assumed to be in backward direction (at step 320).
[0071] At step 306, in case the absolute along the forward axis is not greater than the absolute value along the lateral axis, then the step is assumed to be in the right or the left direction (at step 320).
[0072] At step 322, the method 300 may include storing lateral acceleration values between two consecutive troughs after subtracting the rest average value from the lateral acceleration values. Trough is the minimum point present between two consecutive step detection points in the vertical acceleration data (avert).
[0073] At step 324, the method 300 may include determining maximum values and minimum values from the stored samples of the lateral acceleration data and obtaining position of the maximum values and the minimum values.
[0074] At step 326, the method 300 may include checking if the maximum value is present before the minimum value in the stored samples of the vertical acceleration data (avert), then the step is assumed to be in the left direction (step 328) and if the minimum value is present before the maximum value in the stored samples of the vertical acceleration data, then the step is assumed to be in the right direction (step 330).
[0075] FIG. 4 illustrates a block diagram describing a method for pedestrian navigation, in accordance with an embodiment of the present invention.
[0076] In another embodiment, the present disclosure relates to a method 400 for pedestrian navigation. At block 402, the method 400 may include receiving from the one or more sensors 102, the set of acceleration data pertain to a vertical acceleration data, a forward acceleration data and a lateral acceleration data.
[0077] At block 404, the method 400 may include detecting a number of steps of the pedestrian based on the vertical acceleration data so as to mitigate false step detections caused by multiple peaks in the vertical acceleration data.
[0078] At block 406, the method 400 may include estimating length of each step of the number of steps and calculating the total distance travelled.
[0079] At block 408, the method 400 may include determining from the detected forward acceleration data and the lateral acceleration data, direction of the number of steps of the pedestrian corresponding to the detected step.
[0080] At block 410, the method 400 may further include classifying the direction of the number of steps into a front direction, a back direction, a right direction, and a left direction based on the analysis of the forward acceleration data and the lateral acceleration data.
[0081] At block 412, the method 400 may also include calculating a current position of the pedestrian with respect to a start point, based on the detected number of steps and the direction of the number of steps.
[0082] At block 414, the method 400 may also include displaying the current position of the pedestrian.
[0083] It will be apparent to those skilled in the art that the system 100 of the disclosure may be provided using some or all of the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0084] The present disclosure provides a system for a pedestrian navigation for detection of steps and step direction of a pedestrian.
[0085] The present disclosure provides a system that provide a solution to wrong detection of a single step as multiple steps due to noisy MEMS sensor occur in vertical acceleration signal.
[0086] The present disclosure provides a system that consider non-forward walking motions in order to find accurate position of the user.
[0087] The present disclosure provides a system that utilises vertical, forward and lateral acceleration data for accurately finding the direction of steps taken.
, Claims:1. A system (100) for pedestrian navigation, the system comprising:
a pedometer mounted on a waist of a pedestrian;
one or more sensors (102) configured in the pedometer, the one or more sensors (102) configured to detect a set of acceleration data pertain to a vertical acceleration data, a forward acceleration data and a lateral acceleration data; and
a processing element (120) is interfaced with the one or more sensors (102), the processor element configured to:
receive, at a data collection unit (104), from the one or more sensors (102), the set of acceleration data;
detect, by a step detection unit (110), a number of steps of the pedestrian based on the vertical acceleration data so as to mitigate false step detections caused by multiple peaks in the vertical acceleration data;
estimate, by a stride length estimation unit (112), length of each step of the number of steps and calculate the total distance travelled:
determine, by a pedometry unit (114), from the detected forward acceleration data and the lateral acceleration data, direction of the number of steps of the pedestrian corresponding to the detected step;
classify the direction of the number of steps into a front direction, a back direction, a right direction, and a left direction based on the analysis of the forward acceleration data and the lateral acceleration data;
calculate, by a position calculation unit (116), a current position of the pedestrian with respect to a start point, based on the detected number of steps and the direction of the number of steps; and
display, by a display unit (118), the current position of the pedestrian.

2. The system (100) as claimed in claim 1, wherein the number of steps detected by the pedestrian pertain to slow walk, normal walk, fast walk and running.
3. The system (100) as claimed in claim 1, wherein the processing element (120) configured to determine the number of steps of the pedestrian, the processing element (120) configured to:
smoothen, by a data smoothening unit (106), the set of acceleration data to minimize impact of noisy signals; and
dynamically adjust, by a threshold finding unit (108), a step detection threshold based on characteristics of the vertical acceleration data to determine whether a step has been taken, wherein the step detection threshold is dynamically calculated as an average of a minimum value and a maximum value of a set of samples of the vertical acceleration data.

4. The system (100) as claimed in claim 1, wherein the processing element (120) configured to:
execute a decision-making unit to analyse the set of samples of the vertical acceleration data to detect occurrence of the number of steps, by counting values of the set of samples of the vertical acceleration data that are below the step detection threshold thereby addressing the issue of the multiple peaks occurring in the vertical acceleration data whenever the step is taken; and
determine the step taken by the pedestrian if the count of the values of the vertical acceleration data is greater than a predefined number, wherein the predefined number is set to zero once the step is detected and incremented every time the set of samples of the vertical acceleration data is found below the step detection threshold after a step detection point.

5. The system (100) as claimed in claim 1, wherein the processing element (120) configured to estimate length of each step of the number of steps, the processing element (120) configured to:
utilize the stride length estimation unit (112) to calculate the length of each step by incorporating parameters pertaining to maximum acceleration value measured within the step, minimum acceleration value for the step, average acceleration value for the step; and
accumulate a stride length value for each step to determine the total distance travelled.

6. The system (100) as claimed in claim 1, wherein the processing element (120) configured to analyse the direction of the step to distinguish whether the pedestrian is making the number of steps in the forward-backward direction or in lateral direction, the processing element (120) configured to:
store the set of samples of the forward acceleration data and the lateral acceleration data between two consecutive steps;
find variation of the set of samples of the forward acceleration data and the lateral acceleration data from the stored data;
determine that the pedestrian is making the step in the forward-backward direction, if the variation in the set of samples of the forward acceleration data is greater than the variation in the lateral acceleration data; and
determine that the pedestrian is making the step in the lateral direction, if the variation in the set of samples of the lateral acceleration data is greater than the variation in forward acceleration data.

7. The system (100) as claimed in claim 1, wherein the processing element (120) configured to distinguish between the forward and the backward directions, the processing element (120) configured to:
determine two consecutive trough points in the vertical acceleration data;
store the set of samples of the forward acceleration data present between the determined trough points;
determine an average value of the stored samples of the forward acceleration data;
determine that the pedestrian is making the step in the forward direction, if the average value is lesser than a first or last sample value in the set of samples stored; and
determine that the pedestrian is making the step in the backward direction, if the average value is greater than the first or last sample value in the set of samples stored.

8. The system (100) as claimed in claim 1, wherein the processing element (120) configured to distinguish between the left direction and the right direction, the processing element (120) configured to:
determine the two consecutive trough points in the vertical acceleration data;
store the set of samples of the lateral acceleration data present between the determined trough points;
determine that the pedestrian is making the step in the left direction if in the stored set of samples of the lateral acceleration data, a maximum value is present before a minimum value; and
determine that the pedestrian is making the step in the right direction if in the stored set of samples of the lateral acceleration data, the minimum value is present before the maximum value.

9. The system (100) as claimed in claim 1, wherein the trough point is a last sample below the step detection threshold along falling side of the vertical acceleration data having the step detection point.

10. A method (400) for pedestrian navigation, the method comprising:
receiving (402), at a data collection unit (104), from the one or more sensors (102), the set of acceleration data pertain to vertical acceleration data, forward acceleration data and lateral acceleration data;
detecting (404), by a step detection unit (110), a number of steps of the pedestrian based on the vertical acceleration data so as to mitigate false step detections caused by multiple peaks in the vertical acceleration data;
estimating (406), by a stride length estimation unit (112), length of each step of the number of steps and calculating the total distance travelled;
determining (408), by a pedometry unit (114), from the detected forward acceleration data and the lateral acceleration data, direction of the number of steps of the pedestrian corresponding to the detected step;
classifying (410) the direction of the number of steps into a front direction, a back direction, a right direction, and a left direction based on the analysis of the forward acceleration data and the lateral acceleration data;
calculating (412), by a position calculation unit (116), a current position of the pedestrian with respect to a start point, based on the detected number of steps and the direction of the number of steps; and
displaying (414), by a display unit (118), the current position of the pedestrian.