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Purpose: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices against camera-annotated ground truth. This study aimed to describe the development and validation of step count derived from a wrist-worn accelerometer and assess its association with cardiovascular and all-cause mortality in a large prospective cohort. Methods: We developed and externally validated a self-supervised machine learning step detection model, trained on an open-source and step-annotated free-living dataset. Thirty-nine individuals will free-living ground-truth annotated step counts were used for model development. An open-source dataset with 30 individuals was used for external validation. Epidemiological analysis was performed using 75,263 UK Biobank participants without prevalent cardiovascular disease (CVD) or cancer. Cox regression was used to test the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders. Results: The algorithm substantially outperformed reference models (free-living mean absolute percent error of 12.5% vs 65%–231%). Our data indicate an inverse dose–response association, where taking 6430–8277 daily steps was associated with 37% (25%–48%) and 28% (20%–35%) lower risk of fatal CVD and all-cause mortality up to 7 yr later, compared with those taking fewer steps each day. Conclusions: We have developed an open and transparent method that markedly improves the measurement of steps in large-scale wrist-worn accelerometer datasets. The application of this method demonstrated expected associations with CVD and all-cause mortality, indicating excellent face validity. This reinforces public health messaging for increasing physical activity and can help lay the groundwork for the inclusion of target step counts in future public health guidelines. [ABSTRACT FROM AUTHOR]

Objective: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and disseminating that information via real‐time, intraoperative decision‐making. The objective of the present study was to examine the feasibility and accuracy of a novel computer vision algorithm for hysterectomy surgical step identification. Methods: This was a retrospective study conducted on surgical videos of laparoscopic hysterectomies performed in 277 patients in five medical centers. We used a surgical intelligence platform (Theator Inc.) that employs advanced computer vision and AI technology to automatically capture video data during surgery, deidentify, and upload procedures to a secure cloud infrastructure. Videos were manually annotated with sequential steps of surgery by a team of annotation specialists. Subsequently, a computer vision system was trained to perform automated step detection in hysterectomy. Analyzing automated video annotations in comparison to manual human annotations was used to determine accuracy. Results: The mean duration of the videos was 103 ± 43 min. Accuracy between AI‐based predictions and manual human annotations was 93.1% on average. Accuracy was highest for the dissection and mobilization step (96.9%) and lowest for the adhesiolysis step (70.3%). Conclusion: The results of the present study demonstrate that a novel AI‐based model achieves high accuracy for automated steps identification in hysterectomy. This lays the foundations for the next phase of AI, focused on real‐time clinical decision support and prediction of outcome measures, to optimize surgeon workflow and elevate patient care. Synopsis: AI‐based model achieves high‐accuracy surgical step detection in laparoscopic hysterectomies, paving the way for incorporation of surgical intelligence platforms in gynecology. [ABSTRACT FROM AUTHOR]

Abstract Background Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. Methods We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. Results We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3–13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8–39] ms) and 26 ms (IQR [12–50] ms). Conclusions Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

To solve the problem that the pedestrian dead reckoning (PDR) indoor signals were susceptible to interference from environment and multipath effects, the optimal indoor PDR modelling method based on multi-model fusion was proposed. The system model of the multi-model fusion indoor PDR modelling approach was given with four key stages of step detection, step length projection, direction projection and position projection. In the step detection stage, the peak detection algorithm, local maximum algorithm and advance over zero detection algorithm were integrated, and in the step projection stage, the Weinberg method and Kim method were integrated. The Kalman filter algorithm was used to correct the errors of step detection and step projection. The comparison with traditional algorithms in terms of step number, step length, direction and position errors in different scenarios was completed. The results show that the fused model combines the feature recognition results of traditional step detection and step length estimation algorithms, which can realize the optimization of signal characteristics in the process of step detection and step length estimation. In the handheld scene, the step detection is accurate, and the step length estimation median error range is 0.060 m or less with the minimum direction estimation average absolute error of 3.06° and the position estimation average error of 0.235 3 m, which achieves good indoor walking status recognition and position estimation performance. [ABSTRACT FROM AUTHOR]

Background: Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. Methods: We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. Results: We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3–13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8–39] ms) and 26 ms (IQR [12–50] ms). Conclusions: Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability. [ABSTRACT FROM AUTHOR]

Pedestrian Dead Reckoning (PDR) plays an important role in multi-sensor fusion of indoor positioning due to its autonomy and continuity advantages. The robustness of PDR significantly impacts indoor positioning accuracy, but various pedestrians and mobility contexts pose challenges for reliable step detection and accurate step length estimation. This paper proposes a context-assisted personalized PDR localization solution to address these challenges. Firstly, by exploiting temporal and frequency domain features, an enhanced step detection method is developed to mitigate false step detection, especially during unfavorable actions of pedestrians. Subsequently, a personalized step length model is proposed, and its parameters are dynamically updated online using other high-precision sensors available within a multi-sensor fusion positioning solution. Moreover, the personalized step length model is further refined using mobility context knowledge. Finally, a novel context-assisted pedestrian velocity model is established for PDR localization to enhance positioning accuracy, particularly when there are changes in mobility contexts. The results demonstrate that the robustness of step detection is improved, and the false detection rate is reduced from 13% to 2%. For various smartphone users, the proposed context-assisted personalized step length model exhibits a relative error of 2.01%, in contrast to 7.06% observed with the traditional flat model. Consequently, the accuracy of walking distance is enhanced from 92.2% to 98.9%, and the PDR localization error is reduced from 2.49 m to 1.63 m. Importantly, the proposed solution exhibits more robust and consistent performance across different pedestrians, smartphone models, and challenging mobility contexts.

The existing algorithms for step detection have rarely been designed for walking in complex scenes. Complex scenes often bring about more complicated changes in walking states that could cause ordinary detection algorithms less effective. In this paper, an observation is made that there are gatherings of low-frequency signals, called clusters, in the spectrogram generated from the walking signal in a particular situation through wavelet transform. The clusters would exhibit prominent features when some specific basis function is chosen for the wavelet transform, which can precisely characterize the strides in walking. Then, the criteria for choosing the basis function of wavelet transform are established and verified experimentally. Based on the spectral features, an efficient and accurate step detection algorithm named frequency domain extension detection (FDED) is proposed and its time/space complexity will be no more than the constant times of its input size, O(n). FDED consists of three phases. First, the Kalman filter is adopted to denoise the raw data. Then, a continuous wavelet transform is applied to the filtered data to attain the obvious gait pattern in the time spectrum. Finally, a robust detection algorithm is proposed to implement step counting and single-stride segmentation. The experiments are conducted on two datasets, Diecui, a self-established dataset with diverse walking patterns in complex scenes, and a public dataset, ZJU-gaitacc. The experimental results show that FDED achieves an average accuracy of 99.1% for step counting on Diecui, and outperforms several representative detection algorithms on ZJU-gaitacc, which suggests that the proposed algorithm possesses strong adaptability in complex scenes with diverse personnel. [ABSTRACT FROM AUTHOR]

To complement traditional clinical fall risk assessments, research is oriented towards adding real-life gait-related fall risk parameters (FRP) using inertial sensors fixed to a specific body position. While fixing the sensor position can facilitate data processing, it can reduce user compliance. A newly proposed step detection method, Smartstep, has been proven to be robust against sensor position and real-life challenges. Moreover, FRP based on step variability calculated from stride times (Standard deviation (SD), Coefficient of Variance (Cov), fractal exponent, and sample entropy of stride duration) proved to be useful to prospectively predict the fall risk. To evaluate whether Smartstep is convenient for calculating FRP from different sensor placements. 29 elderly performed a 6-minute walking test with IMU placed on the waist and the wrist. FRP were computed from step-time estimated from Smartstep and compared to those obtained from foot-mounted inertial sensors: precision and recall of the step detection, Root mean square error (RMSE) and Intraclass Correlation Coefficient (ICC) of stride durations, and limits of agreement of FRP. The step detection precision and recall were respectively 99.5% and 95.9% for the waist position, and 99.4% and 95.7% for the wrist position. The ICC and RMSE of stride duration were 0.91 and 54 ms respectively for both the waist and the hand position. The limits of agreement of Cov, SD, fractal exponent, and sample entropy of stride duration are respectively 2.15%, 25 ms, 0.3, 0.5 for the waist and 1.6%, 16 ms, 0.23, 0.4 for the hand. Robust against the elderly's gait and different body locations, especially the wrist, this method can open doors toward ambulatory measurements of steps, and calculation of different discrete stride-related falling risk indicators. • Fall risk parameters (FRP) estimated independently of sensor positions. • Fall risk parameters based on discrete stride time series. • Foot-mounted compared to wrist and waist inertial sensors. • Precision and recall of step detection were 99% and 96% respectively. • Encouraging results for usage in real-life fall risk monitoring. [ABSTRACT FROM AUTHOR]

The prominence of Indoor Navigation Systems (INS) has been on an upward trajectory in recent years. While the Global Positioning System (GPS) commonly utilizes radio waves from artificial satellites for positioning information, its precision is compromised indoors due to potential radio wave obstruction by buildings. In contrast, pedometers, a critical component of INS, can provide invaluable insights into health, exercise, and user itineraries by detecting the number of steps and pinpointing optimal indoor positions. This paper proposes the development of a high-accuracy pedometer system. The proposed tracking system capitalizes on data harvested from accelerometers, sensors integrated into mobile devices, to furnish indoor tracking predicated on a straightforward pedometer approach. Online measurements and tests were conducted in residential settings, and the recorded tests were subsequently simulated offline via MATLAB. The performance of the system was evaluated in a real-world indoor residential scenario using an iPhone6 mobile device, with the discussion encompassing potential usability aspects of the approach. The devised system mitigated the drift of sensor readings by amalgamating the data from the gyroscope and accelerometer. The experimental results revealed a percentage error of 4.33% for the proposed method, translating to an error of 0.65 meters from an average walking distance of approximately 3 meters, out of 15 meters. Future research endeavors will concentrate on enhancing the accuracy of the approach by implementing data filtering and interference reduction techniques. [ABSTRACT FROM AUTHOR]

Precise step detection is an essential task for pedestrian dead reckoning based navigation. This study presents a novel step detection method using a Dynamic Weight Integrated Fuzzy C-Means (DWIFCM) algorithm. It utilizes the statistical criteria extracted from the readings of accelerometer sensors and improves the accuracy of step detection. The performance of the proposed step detection technique is evaluated by considering the variations in walking patterns of the pedestrian in different landscapes of the path. The experimental results yield that the proposed method outperforms among the existing benchmark techniques.

Objectives: To solve the problems in pedestrian dead reckoning algorithms for indoor positioning, the step recognition accuracy for step detection is not high, the synchronous control is not precise, and there is a large location deviation. Methods: An algorithm of improved finite state machine step detection for the activity of flat holding smartphone was proposed. A finite number of states corresponded to the trend of resultant acceleration variation during walking. Step detection and step cycle estimation were realized based on adjacent resultant acceleration difference and several thresholds of climbing and descending times. Two testers conducted experimental tests in 211 m corridors with flat holding smartphone, respectively. Results: Experimental results show that the accuracy of step detection tests is 100% by using the improved algorithm. It is 0.004 s earlier on average than the actual time for each step. Moreover, the average location error is 0.384 m. Compared to the auto‑correction analysis and acceleration differential based on finite state machine algorithms, the accuracy of step recognition, synchronous control, and location estimation are im ‑ proved by at least 0.7%, 60%, and 21.15%, respectively. Conclusions: The improved algorithm behaves better than the existing algorithms in step recognition, synchronous control, and location estimation. [ABSTRACT FROM AUTHOR]

Commercial smartwatches could be useful for step counting and monitoring ambulatory activity. However, in Parkinson's disease (PD) patients, an altered gait, pharmacological condition, and symptoms lateralization may affect their accuracy and potential usefulness in research and clinical routine. Steps were counted during a 6 min walk in 47 patients with PD and 47 healthy subjects (HS) wearing a Garmin Vivosmart 4 (GV4) on each wrist. Manual step counting was used as a reference. An inertial sensor (BTS G-Walk), placed on the lower back, was used to compute spatial-temporal gait parameters. Intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE) were used for accuracy evaluation and the Spearman test was used to assess the correlations between variables. The GV4 overestimated steps in PD patients with only a poor-to-moderate agreement. The OFF pharmacological state and wearing the device on the most-affected body side led to an unacceptable accuracy. The GV4 showed an excellent agreement and MAPE in HS at a self-selected speed, but an unacceptable performance at a slow speed. In PD patients, MAPE was not associated with gait parameters and clinical variables. The accuracy of commercial smartwatches for monitoring step counting might be reduced in PD patients and further influenced by the pharmacological condition and placement of the device. [ABSTRACT FROM AUTHOR]

A popular approach to track the position of a user is to use an inertial measurement unit (IMU), which allows tracking a user with a pedestrian dead reckoning (PDR) system by estimating the length and heading of each step a user takes. Furthermore, a user’s steps together with their length can be used to detect anomalies in the gait due to, e.g., a gait disorder. To determine the length and/or heading of a step, we first need an algorithm that estimates the boundaries of a step, i.e., the start and end, in the IMU signals. In this article, we propose a deep-learning-based step detection algorithm that detects the start and end of a step using only data from a handheld accelerometer. In this algorithm, also, the relationship between the start and end of a step is used to improve performance. To evaluate the performance of the detector, in contrast to most previous works that only look at the total number of detected steps, we determine if the start and end of each step are detected at the correct instant. This resulted in a step detector with an f-score of 99.2% and 99.0% for, respectively, detecting the start and end of a step. After detecting the start and end of the steps, we use a long short-term memory (LSTM)/convolutional neural network (CNN)-based step length estimator, which resulted in a mean absolute error (mae) on the step length of 3.21 cm. Finally, we also determine the combined performance of the step detector and step length estimator, i.e., where the proposed step detector is used to extract the accelerometer fragments of a step instead of assuming that the boundaries are known. This resulted in an mae of 6.56 cm using the deep-learning-based step length estimator. [ABSTRACT FROM AUTHOR]

The traditional pedestrian dead reckoning (PDR) algorithm has low positioning precision due to the accumulated errors of step size and heading, which can not meet the requirements of precise positioning of underground personnel. In order to solve the problem, a PDR algorithm for precise positioning of underground personnel based on long short-term memory (LSTM) personalized step size estimation is proposed. Firstly, the acceleration and gyroscope inertia information in the movement of underground personnel is collected, and the movement distance of each step is calculated to construct step size data. The LSTM model of personalized step size estimation of the underground personnel is obtained through off-line training. Secondly, in the online prediction stage, the underground personnel movement data such as acceleration, gyroscope and geomagnetism are collected in real-time through the mine intrinsically safe smart phone. The underground personnel movement step and step size of each step are obtained by using the step detection algorithm and personalized step size estimation model respectively. The heading angle is obtained by using the Kalman filtering and heading estimation algorithm. Finally, the current position of underground personnel is predicted according to step size estimation and heading angle. In Inner Mongolia Ordos Gaotouyao Coal Mine, the underground personnel movement data is collected for testing, and the results show as follows. The PDR algorithm for precise positioning of underground personnel based on LSTM personalized step size estimation has a step detection precision of 96.5% and a step size prediction precision of 90%. The algorithm has a relative positioning error of 2.33% in the real underground environment, which improves the personnel positioning precision in coal mine.

Background: The monitoring of accelerometry derived load has received increased attention in recent years. However, the ability of such measures to quantify training load during sport-related activities is not well established. Thus, the current study aimed to assess the validity and reliability of tri-axial accelerometers to identify step count and quantify external load during several locomotor conditions including walking, jogging, and running. Method: Thirty physically active college students (height = 176.8 ± 6.1 cm, weight = 82.3 ± 12.8 kg) participated. Acceleration data was collected via two tri-axial accelerometers (Device A and B) sampling at 100 Hz, mounted closely together at the xiphoid process. Each participant completed two trials of straight-line walking, jogging, and running on a 20 m course. Device A was used to assess accelerometer validity to identify step count and the test-retest reliability of the instrument to quantify the external load. Device A and Device B were used to assess inter-device reliability. The reliability of accelerometry-derived metrics Impulse Load (IL) and Magnitude g (MAG) were assessed. Results: The instrument demonstrated a positive predictive value (PPV) ranging between 96.98%–99.41% and an agreement ranging between 93.08%–96.29% for step detection during all conditions. Good test-retest reliability was found with a coefficient of variation (CV) <5% for IL and MAG during all locomotor conditions. Good inter-device reliability was also found for all locomotor conditions (IL and MAG CV < 5%). Conclusion: This research indicates that tri-axial accelerometers can be used to identify steps and quantify external load when movement is completed at a range of speeds. [ABSTRACT FROM AUTHOR]

Background: The monitoring of accelerometry derived load has received increased attention in recent years. However, the ability of such measures to quantify training load during sport-related activities is not well established. Thus, the current study aimed to assess the validity and reliability of tri-axial accelerometers to identify step count and quantify external load during several locomotor conditions including walking, jogging, and running.Method: Thirty physically active college students (height = 176.8 ± 6.1 cm, weight = 82.3 ± 12.8 kg) participated. Acceleration data was collected via two tri-axial accelerometers (Device A and B) sampling at 100 Hz, mounted closely together at the xiphoid process. Each participant completed two trials of straight-line walking, jogging, and running on a 20 m course. Device A was used to assess accelerometer validity to identify step count and the test-retest reliability of the instrument to quantify the external load. Device A and Device B were used to assess inter-device reliability. The reliability of accelerometry-derived metrics Impulse Load (IL) and Magnitude g (MAG) were assessed.Results: The instrument demonstrated a positive predictive value (PPV) ranging between 96.98%–99.41% and an agreement ranging between 93.08%–96.29% for step detection during all conditions. Good test-retest reliability was found with a coefficient of variation (CV)

Our aim was to compare three research-grade accelerometers for their accuracy in step detection and energy expenditure (EE) estimation in a laboratory setting, at different speeds, especially in overweight/obese participants. Forty-eight overweight/obese subjects participated. Participants performed an exercise routine on a treadmill with six different speeds (1.5, 3, 4.5, 6, 7.5, and 9 km/h) for 4 min each. The exercise was recorded on video and subjects wore three accelerometers during the exercise: Sartorio Xelometer (SX, hip), activPAL (AP, thigh), and ActiGraph GT3X (AG, hip), and energy expenditure (EE) was estimated using indirect calorimetry for comparisons. For step detection, speed-wise mean absolute percentage errors for the SX ranged between 9.73–2.26, 6.39–0.95 for the AP, and 88.69–2.63 for the AG. The activPALs step detection was the most accurate. For EE estimation, the ranges were 21.41–15.15 for the SX, 57.38–12.36 for the AP, and 59.45–28.92 for the AG. All EE estimation errors were due to underestimation. All three devices were accurate in detecting steps when speed exceeded 4 km/h and inaccurate in EE estimation regardless of speed. Our results will guide users to recognize the differences, weaknesses, and strengths of the accelerometer devices and their algorithms. [ABSTRACT FROM AUTHOR]

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding window and an adaptive threshold is proposed. In this method, the dynamic updating algorithm of a peak threshold is developed, and the minimum peak value filtered after a sliding window filter is used as the adaptive peak threshold, which solves the problem that the peak threshold of different motion states is difficult to update adaptively. Then, a variable sliding window collaborative time threshold method is proposed, which solves the problem that the adjacent windows cannot be contacted, and the initial peak and the end peak are difficult to accurately identify. To evaluate the performance of the proposed unconstrained step detection algorithm, 50 experiments in constrained and unconstrained states are conducted by 25 volunteers holding 21 different types of smartphones. Experimental results show: The average step counting accuracy of the proposed unconstrained step detection algorithm is over 98%. Compared with the open source program Stepcount, the average step counting accuracy of the proposed algorithm is improved by 10.0%. The smartphone-based unconstrained step detection fusing a variable sliding window and an adaptive threshold has a strong ability to adapt to complex unconstrained states, and the average step counting accuracy rate is only 0.6% lower than that of constrained states. This algorithm has a wide audience and is friendly for different genders and smartphones with different prices. [ABSTRACT FROM AUTHOR]

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

Robust and accurate step counting plays an essential role in some fields, such as indoor positing, behavior recognition, and health management. Currently, there are numerous applications or methods in step counting. However, most solutions did not emphasize the elimination of false steps in the non-walking state, which still encounters the over-counting or under-counting problem. In this study, a robust and accurate step counting solution based on movement mode recognition is proposed. First, according to the characteristics of pedestrians walking, the SVM and FSM-DT classifiers were constructed to recognize the user’s motion state and phone usage mode, respectively. The purpose of classification is to solve the step-counting error of non-walking state and initialize a suitable parameter for different cases. Then, we utilized a crest-valley detection algorithm with multi-feature constrained to detect the step, and the initial threshold values for each mode can be adjusted adaptively during walking. The results indicate that the accuracy of the proposed method can reach 99.11% and 97.43% in normal and free walking. Compared with standard peak detection and an excellent method, the proposed method can improve by 44.14% and 18.71% for false walking, respectively, which is a significant improvement in the robustness and accuracy. Furthermore, we also achieve an average accuracy higher than 98% in some typically carrying modes and ways, and higher accuracy compared with four popular step-counting mobile applications in different walking states. [ABSTRACT FROM AUTHOR]

Abstract Background Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. Methods Participants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. Results The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%), slalom walking (IC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%), and turning (IC: recall $$\ge$$ ≥ 85%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 91%; FC: recall $$\ge$$ ≥ 84%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 89%). Conclusions Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

The availability of inertial navigation sensors in smartphones has facilitated the development of pedestrian dead reckoning (PDR) models on a large scale. These models often consist of a step detection algorithm combined with a heading estimation routine. Common approaches to step detection include searching for peaks/valleys in the acceleration signal, principle frequency estimation, and machine learning techniques. Since the sensors embedded in smart devices are prone to noise, the position error grows unbounded if unchecked and requires periodical corrections based on external measurements. In this work, we propose a novel step detection algorithm based on sine-wave approximation of the acceleration signal. This method detects step fractions as well as full steps, which allows for continuous and real-time updates. The step detection algorithm is combined with a heading estimation routine described in our previous work to obtain a stand-alone PDR model. To mitigate error accumulation, we fuse the proposed model with position and heading measurements provided by a commercial indoor positioning system based on ultrasound. We evaluate the performance of the PDR and fused model in an open office environment, by walking along a trajectory while carrying a smartphone in hand or in the pocket. The results demonstrate the feasibility of the sine-wave approximation approach to step detection, as well as the expected benefits of fusing PDR with the ultrasonic system. [ABSTRACT FROM AUTHOR]

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Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects’ habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.

Commercial smartwatches could be useful for step counting and monitoring ambulatory activity. However, in Parkinson’s disease (PD) patients, an altered gait, pharmacological condition, and symptoms lateralization may affect their accuracy and potential usefulness in research and clinical routine. Steps were counted during a 6 min walk in 47 patients with PD and 47 healthy subjects (HS) wearing a Garmin Vivosmart 4 (GV4) on each wrist. Manual step counting was used as a reference. An inertial sensor (BTS G-Walk), placed on the lower back, was used to compute spatial-temporal gait parameters. Intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE) were used for accuracy evaluation and the Spearman test was used to assess the correlations between variables. The GV4 overestimated steps in PD patients with only a poor-to-moderate agreement. The OFF pharmacological state and wearing the device on the most-affected body side led to an unacceptable accuracy. The GV4 showed an excellent agreement and MAPE in HS at a self-selected speed, but an unacceptable performance at a slow speed. In PD patients, MAPE was not associated with gait parameters and clinical variables. The accuracy of commercial smartwatches for monitoring step counting might be reduced in PD patients and further influenced by the pharmacological condition and placement of the device.

In this article, we introduce a pedestrian dead reckoning (PDR)-based navigation device that does not require global navigation satellite system (GNSS) signals or beacons and works with an inertial measurement unit (IMU) mounted on its waist belt. The system identifies the individual by their walking pattern to use the proper gains in the computations, estimates the attitude by applying an unscented Kalman filter, and finally derives the position in three dimensions with the help of a step detection algorithm. The experimental results show an outdoor 4.7-km walk resulting in an error of 0.96%. [ABSTRACT FROM AUTHOR]

• A new algorithm for automatic stride detection during walking and stair climbing. • A new algorithm based on combined data of foot and shank-mounted IMUs. • Performance advance during ascending and descending stairs of the new algorithm. Stride detection, or the identification of the initial (IC) and terminal contact (TC) of the feet while walking, is important for gait analysis. Automatic stride detection based only on kinematic data is challenging, even when using portable, low-cost, user-friendly Inertial Measurement Units (IMUs). Although there are algorithms for straight walking available, they are often not applicable to other movement patterns. Furthermore, these algorithms are based on the use of different IMUs placed on different locations of the body with different pre-processing filters and rely on analyzing different measurement signals. Therefore, it is difficult to apply existing algorithms for specific study settings. To achieve a new algorithm, thirty-five healthy participants were analyzed during walking and stair climbing while kinematic motion data was measured using the IMU system MyoMotion. Based on the analysis of different published methods for IC and TC detection, a new robust stride detection algorithm was developed and validated in comparison with two different algorithms. From this, it was determined that the newly developed algorithm was successful in automatic stride detection during walking and ascending/ descending stairs with 100% detected gait events, while the other algorithms failed during stair climbing with only 44% and 91% detected gait events. [ABSTRACT FROM AUTHOR]

Abstract Background Physical therapy interventions for ambulatory youth with cerebral palsy (CP) often focus on activity-based strategies to promote functional mobility and participation in physical activity. The use of activity monitors validated for this population could help to design effective personalized interventions by providing reliable outcome measures. The objective of this study was to devise a single-sensor based algorithm for locomotion and cadence detection, robust to atypical gait patterns of children with CP in the real-life like monitoring conditions. Methods Study included 15 children with CP, classified according to Gross Motor Function Classification System (GMFCS) between levels I and III, and 11 age-matched typically developing (TD). Six IMU devices were fixed on participant’s trunk (chest and low back/L5), thighs, and shanks. IMUs on trunk were independently used for development of algorithm, whereas the ensemble of devices on lower limbs were used as reference system. Data was collected according to a semi-structured protocol, and included typical daily-life activities performed indoor and outdoor. The algorithm was based on detection of peaks associated to heel-strike events, identified from the norm of trunk acceleration signals, and included several processing stages such as peak enhancement and selection of the steps-related peaks using heuristic decision rules. Cadence was estimated using time- and frequency–domain approaches. Performance metrics were sensitivity, specificity, precision, error, intra-class correlation coefficient, and Bland-Altman analysis. Results According to GMFCS, CP children were classified as GMFCS I (n = 7), GMFCS II (n = 3) and GMFCS III (n = 5). Mean values of sensitivity, specificity and precision for locomotion detection ranged between 0.93–0.98, 0.92–0.97 and 0.86–0.98 for TD, CP-GMFCS I and CP-GMFCS II-III groups, respectively. Mean values of absolute error for cadence estimation (steps/min) were similar for both methods, and ranged between 0.51–0.88, 1.18–1.33 and 1.94–2.3 for TD, CP-GMFCS I and CP-GMFCS II-III groups, respectively. The standard deviation was higher in CP-GMFCS II-III group, the lower performances being explained by the high variability of atypical gait patterns. Conclusions The algorithm demonstrated good performance when applied to a wide range of gait patterns, from normal to the pathological gait of highly affected children with CP using walking aids.

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding window and an adaptive threshold is proposed. In this method, the dynamic updating algorithm of a peak threshold is developed, and the minimum peak value filtered after a sliding window filter is used as the adaptive peak threshold, which solves the problem that the peak threshold of different motion states is difficult to update adaptively. Then, a variable sliding window collaborative time threshold method is proposed, which solves the problem that the adjacent windows cannot be contacted, and the initial peak and the end peak are difficult to accurately identify. To evaluate the performance of the proposed unconstrained step detection algorithm, 50 experiments in constrained and unconstrained states are conducted by 25 volunteers holding 21 different types of smartphones. Experimental results show: The average step counting accuracy of the proposed unconstrained step detection algorithm is over 98%. Compared with the open source program Stepcount, the average step counting accuracy of the proposed algorithm is improved by 10.0%. The smartphone-based unconstrained step detection fusing a variable sliding window and an adaptive threshold has a strong ability to adapt to complex unconstrained states, and the average step counting accuracy rate is only 0.6% lower than that of constrained states. This algorithm has a wide audience and is friendly for different genders and smartphones with different prices.

As for the low-cost and accuracy benefits of integrating Artificial Intelligence (AI) technologies in smartphones, the embedded smart sensors have been utilized in indoor localization and distance estimation applications rather than the requirement of wearable sensors. However, the effect of a potential noise during the user's movement is still an issue, i.e. irrelevant movements. Knowing that the noise can severely impair the accuracy of distance estimation by increasing or decreasing the number of estimated steps. Therefore, this paper proposes a distance estimation method based on dynamic step size and shape. It aims at removing the noise from the bit signal and detecting the number of steps regardless of the noise using the magnitude value for the three-axis accelerometer data. Furthermore, it involves the variables of the user's height with relation to the step size to estimate the walking distance. The experiment sets allow four phases including data collection and filtering, peak detection, dynamic step size detection, and distance estimation. The low-pass filter is applied to remove the resultant noise of irrelevant movements such as handshaking, texting, swinging, etc. The experimental results showed that the proposed method of step detection, step size, and walking distance estimations achieved significant accuracy improvement over existing current approaches reaches (98%) when a pedestrian was walking in multiple mode changes. [ABSTRACT FROM AUTHOR]

Background: Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson's disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions.Methods: Participants (older adults, people with Parkinson's disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories.Results: The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%), slalom walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%), and turning (IC: recall [Formula: see text] 85%, precision [Formula: see text] 95%, F1 score [Formula: see text]91%; FC: recall [Formula: see text] 84%, precision [Formula: see text] 95%, F1 score [Formula: see text]89%).Conclusions: Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events. [ABSTRACT FROM AUTHOR]

Step detection is very important for the foot-mounted Pedestrian Dead Reckoning (PDR) system. To deal with the false detection caused by variant stepping modes and occasional disturbance, a novel robust step detection algorithm is proposed. The proposed algorithm detects the number of steps and zero-velocity phase using data from inertial sensors mounted on the user’s foot. It includes two parts: a novel anti-fault detection algorithm and a novel zero-velocity detection method, which are used to reduce the over-detection and miss-detection of steps, respectively. Field tests show that the proposed approach is robust under varying motion speeds in different step modes for different persons and the average accuracy of the proposed algorithm is 99.94% in all motion modes, which significantly exceeds conventional estimation approaches in terms of step detection accuracy. [ABSTRACT FROM AUTHOR]

Locomotion is an important welfare and health trait in turkey production. Current breeding values for locomotion are often based on subjective scoring. Sensor technologies could be applied to obtain objective evaluation of turkey gait. Inertial measurement units (IMUs) measure acceleration and rotational velocity, which makes them attractive devices for gait analysis. The aim of this study was to compare three different methods for step detection from IMU data from turkeys. This is an essential step for future feature extraction for the evaluation of turkey locomotion. Data from turkeys walking through a corridor with IMUs attached to each upper leg were annotated manually. We evaluated change point detection, local extrema approach, and gradient boosting machine in terms of step detection and precision of start and end point of the steps. All three methods were successful in step detection, but local extrema approach showed more false detections. In terms of precision of start and end point of steps, change point detection performed poorly due to significant irregular delay, while gradient boosting machine was most precise. For the allowed distance to the annotated steps of 0.2 s, the precision of gradient boosting machine was 0.81 and the recall was 0.84, which is much better in comparison to the other two methods (

In last decade, expansion of mobile phones increased people’s usage of these devices. In parallel with the increasing usage rate, the number of developed applications increased for mobile phones. Thanks to mobile phones, many different fields have been affected. However, the biggest contributions of mobile phones are orientation and location finder services. Although people spend major time of their life in indoor areas, usage of navigation systems must be outdoors since these systems get coordinate information from satellites. In indoor areas, walls, and any object in a building cause wrong results or block navigation systems. In this study, orientation and location finder services for indoor navigation will be done by using accelerometer, compass and camera that have been already included in the phones and augmented reality technology for iOS based mobile phones and locations will be provided thanks to beacon Bluetooth devices.

As an infrastructure-free positioning and navigation method, pedestrian dead reckoning (PDR) is still a research hotspot in the field of indoor localization. Step detection (SD) and stride length estimation (SLE) are two key components of PDR, and it is a challenging problem to apply SD and SLE to different walking patterns. Focusing on this problem, this paper proposes a robust SD and SLE method based on recognizing three walking patterns (i.e., Normal Walk, March in Place, and Quick Walk) using a smartphone. First, we propose a dynamic time warping–based peak prediction with zero-crossing detection to improve the SD accuracy. In particular, the proposed SD can accurately identify the starting and ending points of each step in the three walking patterns. Second, according to the extracted features of each step, a random forest algorithm with classification proofreading is used to recognize the three walking patterns. Finally, an improved SLE model is proposed for the different walking patterns to achieve a higher SLE accuracy. The experimental results show that, on average, the SD accuracy is about 97.9%, the recognition accuracy is about 98.4%, and the relative error of the estimated walking distance is about 3.0%, which outperforms those of the existing commonly used SD and SLE methods. [ABSTRACT FROM AUTHOR]

Locomotion is an important welfare and health trait in turkey production. Current breeding values for locomotion are often based on subjective scoring. Sensor technologies could be applied to obtain objective evaluation of turkey gait. Inertial measurement units (IMUs) measure acceleration and rotational velocity, which makes them attractive devices for gait analysis. The aim of this study was to compare three different methods for step detection from IMU data from turkeys. This is an essential step for future feature extraction for the evaluation of turkey locomotion. Data from turkeys walking through a corridor with IMUs attached to each upper leg were annotated manually. We evaluated change point detection, local extrema approach, and gradient boosting machine in terms of step detection and precision of start and end point of the steps. All three methods were successful in step detection, but local extrema approach showed more false detections. In terms of precision of start and end point of steps, change point detection performed poorly due to significant irregular delay, while gradient boosting machine was most precise. For the allowed distance to the annotated steps of 0.2 s, the precision of gradient boosting machine was 0.81 and the recall was 0.84, which is much better in comparison to the other two methods (<0.61). At an allowed distance of 1 s, performance of the three models was similar. Gradient boosting machine was identified as the most accurate for signal segmentation with a final goal to extract information about turkey gait; however, it requires an annotated training dataset. [ABSTRACT FROM AUTHOR]

Basic human activity recognition (HAR) and analysis is becoming a key aspect of tracking and identifying daily habits that can have a critical impact on healthy lifestyles by providing feedback on health status and warning of deterioration. However, current approaches for detecting basic activities such as movements or steps rely on solutions with multiple sensors which affect their size and power consumption. In this paper, we propose a novel method that uses only a single magnetic field sensor for basic step detection, unlike the well-known multisensory solutions. The approach presented here is based on real-time analysis of magnetic field sensor measurements to detect and count steps during a walking activity. The approach is implemented in a system that integrates a digital magnetic field sensor with software blocks: filter, steady state detector, extrema detector with classifier, and threshold comparator implemented in an embedded platform. Outdoor experiments with volunteers of different ages and genders walking at variable speeds showed that the proposed detection method achieves up to 98% accuracy in step detection. The obtained results show that a single magnetic field sensor can be used to detect steps, and in general offers the possibility of simplifying the current solutions by reducing the device dimensions, the cost of a system and its power consumption.

In waist-worn pedestrian navigation systems, the peak detection method accuracy will be reduced when pedestrian carries on activities ups and downs in-situ such as stooped to and fro,squatting up and down. To compensate this error,a pedestrian step detection method aided with angular velocity is proposed based on the analysis on body waist motion feature. This method first extracts body’s forward angular velocity and lateral one to judge whether pedestrian walks ahead. Then peak signal of vertical accelerometer output is used to detect steps in common. In 27 experiments,which pedestrian walked certain steps and interference of activities ups and downs in-situ were brought in,the average accuracy rate of peak detection method and this method was 85. 8%,98. 84% respectively. Experimental results demonstrate that the proposed method can effectively reduce step detection errors generated by walking ups and downs in-situ with a high reliability.

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In this paper, we develop a foot‐mounted pedestrian navigation system prototype with the emphasis on distance measuring with an inertial measurement unit (IMU) which implies the characteristics of pedestrian gait cycle and thus can be used as a crucial step indicator for distance calculation. Conventional methods for step detection and step length estimation cannot adapt well to the general pedestrian applications since the parameters in these methods may vary for different persons and motions. In this paper, an adaptive time‐ and frequency‐domains joint distance measuring method is proposed by utilizing the means of behaviors classification. Two key issues are studied: step detection and step length determination. For the step detection part, first behavior classification along with state transition strategy is designed to identify typical pedestrian behaviors including standing still, walking, running and irregular swing. Then a four‐stage step detection method is proposed to adaptively determine both step frequency and threshold in a flexible window. Based on the behavior classification results, a two‐segment functional based step length model is established to adapt the walking and running behaviors. Finally, real experiments are carried out to verify our proposed step detection method and step length model. The results show that the proposed method outperforms the existing representative methods and it exhibits the merits of accuracy and adaptability for different persons in real time and significantly improves the accuracy of distance measuring. [ABSTRACT FROM AUTHOR]

Based on the heuristic segmentation algorithm, we introduce ztest and standard normal homogeneity(SNHT) test, proposes a new step detection method, which is applied to 20 CMONOC (CrustalMovement Observation Network of China) and 10 IGS (International GNSS Service) reference stations for nearly 5 years of coordinate time series. For the known step of IGS stations, the detection results show that the average accurate detection rates for three components of E, N, U are 68.5%, 71.3% and 64.8%, respectively, and we use the difference of the initial fitting residuals with average of 25 days before and after the step respectively, to repair the coordinates time series, the results are presented in good continuity. Based on the coordinate time series after eliminating the gross error, for the combination of known and detected step epochs, the results of estimation from coordinate time series analysis software show that: The mean standard deviation of velocity between the estimated velocity and the published velocity of CMONOC's stations for the E, N and U directions of all stations are 2.86 mm/a, 1.14 mm/a, 2.31 mm/a, respectively, which are significantly smaller than the average velocity standard deviation obtained by a combination of known and manually picked step epochs. The above results show that the improved heuristic segmentation algorithm can be applied to detect the step of coordinate time series. [ABSTRACT FROM AUTHOR]