Highly accurate step counting at variouswalking states using low-cost inertial measurement unit support indoor positioning system

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01)

Inter-leg distance measurement as a tool for accurate step counting in patients with multiple sclerosis

Step detection is commonly performed using wearable inertial devices. However, methods based on the extraction of signals features may deteriorate their accuracy when applied to very slow walkers with abnormal gait patterns. The aim of this study is to test and validate an innovative step counter method (DiSC) based on the direct measurement of inter-leg distance. Data were recorded using an innovative wearable system which integrates a magneto-inertial unit and multiple distance sensors (DSs) attached to the shank. The method allowed for the detection of both left and right steps using a single device and was validated on thirteen people affected by multiple sclerosis (0 < EDSS < 6.5) while performing a six-minute walking test. Two different measurement ranges for the distance sensor were tested (DS 200 : 0–200 mm; DS 400 : 0–400 mm). Accuracy was evaluated by comparing the estimates of the DiSC method against video recordings used as gold standard. Preliminary results showed a good accuracy in detecting steps with half the errors in detecting the step of the instrumented side compared to the non-instrumented (mean absolute percentage error 2.4% vs 4.8% for DS 200 ; mean absolute percentage error 2% vs 5.4% for DS 400 ). When averaging errors across patients, over and under estimation errors were compensated, and very high accuracy was achieved (E % <1.2% for DS 200 ; E % <0.7% for DS 400 ). DS 400 is the suggested configuration for patients walking with a large base of support

Development Of An Advanced Step Counting Algorithm With Integrated Activity Detection For Free Living Environments

Physical activity plays a crucial role in maintaining overall health and reducing the risk of various chronic diseases. Step counting has emerged as a popular method for assessing physical activity levels, given its simplicity and ease of use. However, accurately measuring step counts in free-living environments presents significant challenges, with most activity trackers exhibiting a percent error above 20%. This study aims to address these challenges by creating a machine learning algorithm that leverages activity labels to improve step count accuracy in real-world conditions. Two approaches to balancing data were used: one employed a simpler oversampling technique, while the other adopted a more nuanced approach involving the removal of outliers. Models 1 and 2 were trained on each of these uniquely balanced datasets. Model 1 performed much better than Model 2 on testing datasets, but both achieved better than 20% error on new datasets, indicating their potential for more accurate step counting in real-world conditions. Despite challenges such as data imbalance, the study demonstrated the viability of using activity labels to enhance step counting accuracy. Future research should focus on addressing data imbalances and exploring more advanced machine learning techniques for more reliable activity monitoring

The feasibility of using pedometers and brief advice to increase activity in sedentary older women:a pilot study

Background: People over the age of 70 carry the greatest burden of chronic disease, disability and health care use. Participation in physical activity is crucial for health, and walking accounts for much of the physical activity undertaken by sedentary individuals. Pedometers are a useful motivational tool to encourage increased walking and they are cheap and easy to use. The aim of this pilot study was to evaluate the feasibility of the use of pedometers plus a theory-based intervention to assist sedentary older women to accumulate increasing amounts of physical activity, mainly through walking. Methods: Female participants over the age of 70 were recruited from primary care and randomised to receive either pedometer plus a theory-based intervention or a theory-based intervention alone. The theory-based intervention consisted of motivational techniques, goal-setting, barrier identification and self-monitoring with pedometers and daily diaries. The pedometer group were further randomised to one of three target groups: a 10%, 15% or 20% monthly increase in step count to assess the achievability and acceptability of a range of targets. The primary outcome was change in daily activity levels measured by accelerometry. Secondary outcome measures were lower limb function, health related quality of life, anxiety and depression. Results: 54 participants were recruited into the study, with an average age of 76. There were 9 drop outs, 45 completing the study. All participants in the pedometer group found the pedometers easy to use and there was good compliance with diary keeping (96% in the pedometer group and 83% in the theory-based intervention alone group). There was a strong correlation (0.78) between accelerometry and pedometer step counts i.e. indicating that walking was the main physical activity amongst participants. There was a greater increase in activity (accelerometry) amongst those in the 20% target pedometer group compared to the other groups, although not reaching statistical significance (p = 0.192). Conclusion: We have demonstrated that it is feasible to use pedometers and provide theory-based advice to community dwelling sedentary older women to increase physical activity levels and a larger study is planned to investigate this further.Publisher PDFPeer reviewe

Self-supervised machine learning to characterise step counts from wrist-worn accelerometers in the UK Biobank

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 aims 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. 39 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%, versus 65-231%). Our data indicate an inverse dose-response association, where taking 6,430-8,277 daily steps was associated with 37% [25-48%] and 28% [20-35%] lower risk of fatal CVD and all-cause mortality up to seven years later, compared to 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

Accuracy of a smartphone pedometer application according to different speeds and mobile phone locations in a laboratory context

Background - The purpose of this study was to compare the accuracy of a smartphone application and a mechanical pedometer for step counting at different walking speeds and mobile phone locations in a laboratory context. Methods - Seventeen adults wore an iPphone6© with Runtastic Pedometer© application (RUN), at 3 different locations (belt, arm, jacket) and a pedometer (YAM) at the waist. They were asked to walk on an instrumented treadmill (reference) at various speeds (2, 4 and 6 km/h). Results - RUN was more accurate than YAM at 2 km/h (p &lt; 0.05) and at 4 km/h (p = 0.03). At 6 km/h the two devices were equally accurate. The precision of YAM increased with speed (p &lt; 0.05), while for RUN, the results were not significant but showed a trend (p = 0.051). Surprisingly, YAM underestimates the number of step by 60.5% at 2 km/h. The best accurate step counting (0.7% mean error) was observed when RUN is attached to the arm and at the highest speed. Conclusions - RUN pedometer application could be recommended mainly for walking sessions even for low walking speed. Moreover, our results confirm that the smartphone should be strapped close to the body to discriminate steps from noise by the accelerometers (particularly at low speed)

Have pedometer, will travel

Advise your patients to use a pedometer, set a step goal, and keep a step diary. This simple intervention takes only a few moments and is effective in increasing patients' physical activity and decreasing both body mass index (BMI) and systolic blood pressure. Stength of recommendation: A: Based on a meta-analysis of randomized controlled trials (RCTs) and observational studies

A Comparison of Commonly Used Accelerometer Based Activity Monitors in Controlled and Free-Living Environment

This dissertation was designed to determine the effects of body mass index (BMI) and walking speed on activity monitor outputs. A secondary purpose was to compare the activity monitors’ performance in a free-living environment. In the first experiment, 71 participants wore three waist-mounted activity monitors (Actical, ActiGraph, and NL-2000) and an ankle-mounted device (StepWatch 3) while walking on a treadmill (40, 67 and 94 m/min). The tilt angle of each device was measured. The Actical recorded 26% higher activity counts (P \u3c 0.01) in obese persons with a tilt \u3c10 degrees, compared to normal weight persons. The ActiGraph was unaffected by BMI or tilt angle. In the second experiment, the steps recorded by the devices were compared to actual steps. Speed had the greatest influence on the accuracy these devices. At 40 m/min, the ActiGraph was the least accurate device for normal weight (38%), overweight (46%) and obese (48%) individuals. The Actical, NL-2000 and StepWatch averaged 65%, 73% and 99% of steps taken, respectively. Lastly, several generations of the ActiGraph (7164, GT1M, and GT3X), and other research grade activity monitors (Actical; ActivPAL; and Digi-Walker) were compared to a criterion measure of steps. Fifty-six participants performed treadmill walking (40, 54, 67, 80 and 94 m/min) and wore the devices for 24-hours under free-living conditions. BMI did not affect step count accuracy during treadmill walking. The StepWatch, PAL, and the AG7164 were the most accurate across all speeds; the other devices were only accurate at the faster speeds. In the free-living environment, all devices recorded about 75% of StepWatch-determined steps, except the AG7164 (99%). Based on these findings, we conclude that BMI does not affect the output of these activity monitors. However, waist-borne activity monitors are highly susceptible to under-counting steps at walking speeds below 67 m/min, or stepping rates below 100 steps/min. An activity monitor worn on the ankle is less susceptible to these speed effects and provides the greatest accuracy for step counting

Rehearsal and pedometer reactivity in children.

The main purpose of this study was to investigate whether rehearsal, defined as the tendency to recurrently ruminate over upsetting aversive experiences, had an effect on pedometry reactivity. A total of 156 Hong Kong Chinese children aged 9–12 years were recruited. Participants completed the Rehearsal Scale for Children-Chinese (RSC-C; Ling, Maxwell, Masters, & McManus, 2010) and wore the pedometers for 3 consecutive weeks. The mean number of steps was significantly higher in Week 1 than in Week 3. High rehearsers showed a larger decrease in mean number of steps from Week 1 to Week 3 than low rehearsers. Future physical activity intervention studies should adjust for reactivity in their baseline measurements and should further examine the relationship between habitual PA and individual propensities for rehearsal