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The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions.

The purpose of this study was to classify runners in sex-specific groups as either competitive or recreational based on center of mass (CoM) accelerations. Forty-one runners participated in the study (25 male and 16 female), and were labeled as competitive or recreational based on age, sex, and race performance. Three-dimensional acceleration data were collected during a 5-minute treadmill run, and 24 features were extracted. Support vector machine classification models were used to examine the utility of the features in discriminating between competitive and recreational runners within each sex-specific subgroup. Competitive and recreational runners could be classified with 82.63 % and 80.4 % in the male and female models, respectively. Dominant features in both models were related to regularity and variability, with competitive runners exhibiting more consistent running gait patterns, but the specific features were slightly different in each sex-specific model. Therefore, it is important to separate runners into sex-specific competitive and recreational subgroups for future running biomechanical studies. In conclusion, we have demonstrated the ability to analyze running biomechanics in competitive and recreational runners using only CoM acceleration patterns. A runner, clinician, or coach may use this information to monitor how running patterns change as a result of training.

The incidence of patellofemoral pain (PFP) is 2 times greater in females compared with males of similar activity levels; however, the exact reason for this discrepancy remains unclear. Abnormal mechanics of the hip and knee in the sagittal, frontal, and transverse planes have been associated with an increased risk of PFP. The purpose of this study was to compare the mechanics of the lower extremity in males and females during running in order to better understand the reason(s) behind the sex discrepancy in PFP. Three-dimensional kinematic and kinetic data were collected as male and female participants completed overground running trials at a speed of 4.0 m · s (±5%). Patellofemoral joint stress (PFJS) was estimated using a sagittal plane knee model. The kinematics of the hip and knee in the frontal and transverse planes were also analysed. Male participants demonstrated significantly greater sagittal plane peak PFJS in comparison with the female participants (P < .001, ES = 1.9). However, the female participants demonstrated 3.5° greater peak hip adduction and 3.4° greater peak hip internal rotation (IR). As a result, it appears that the sex discrepancy in PFP is more likely to be related to differences in the kinematics of the hip in the frontal and transverse planes than differences in sagittal plane PFJS.

The purpose of this study was to use wearable technology data to quantify alterations in subject-specific running patterns throughout a marathon race and to determine if runners could be clustered into subgroups based on similar trends in running gait alterations throughout the marathon. Using a wearable sensor, data were collected for cadence, braking, bounce, pelvic rotation, pelvic drop, and ground contact time for 27 runners. A composite index was calculated based on the “typical” data (4–14 km) for each runner and evaluated for 14 individual 2-km sections thereafter to detect “atypical” data (ie, higher indices). A cluster analysis assigned all runners to a subgroup based on similar trends in running alterations. Results indicated that the indices became significantly higher starting at 20 to 22 km. Cluster 1 exhibited lower indices than cluster 2 throughout the marathon, and the only significant difference in characteristics between clusters was that cluster 1 had a lower age–grade performance score than cluster 2. In summary, this study presented a novel method to investigate the effects of fatigue on running biomechanics using wearable technology in a real-world setting. Recreational runners with higher age–grade performance scores had less atypical running patterns throughout the marathon compared with runners with lower age–grade performance scores.

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