Accelerometry: a technique for quantifying movement patterns during walking.

Effect of low pass filtering on joint moments from inverse dynamics: implications for injury prevention.

AB Purpose: Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities. Methods: Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. Results: With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. Conclusions: A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.

Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle

The development of miniature sensors that can be unobtrusively attached to the body or can be part of clothing items, such as sensing elements embedded in the fabric of garments, have opened countless possibilities of monitoring patients in the field over extended periods of time. This is of particular relevance to the practice of physical medicine and rehabilitation. Wearable technology addresses a major question in the management of patients undergoing rehabilitation, i.e. have clinical interventions a significant impact on the real life of patients? Wearable technology allows clinicians to gather data where it matters the most to answer this question, i.e. the home and community settings. Direct observations concerning the impact of clinical interventions on mobility, level of independence, and quality of life can be performed by means of wearable systems. Researchers have focused on three main areas of work to develop tools of clinical interest: 1)the design and implementation of sensors that are minimally obtrusive and reliably record movement or physiological signals, 2)the development of systems that unobtrusively gather data from multiple wearable sensors and deliver this information to clinicians in the way that is most appropriate for each application, and 3)the design and implementation of algorithms to extract clinically relevant information from data recorded using wearable technology. Journal of NeuroEngineering and Rehabilitation has devoted a series of articles to this topic with the objective of offering a description of the state of the art in this research field and pointing to emerging applications that are relevant to the clinical practice in physical medicine and rehabilitation.

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Advances in wearable technology and applications in physical medicine and rehabilitation.

http://wisitmax.com/img/researchexp/stanford/wearablegait/2014_GaitAndPosture.pdf

Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention

Handling of impact forces in inverse dynamics

Objective: The aetiology of patellar tendinopathy (jumper’s knee) remains unclear. To see whether landing strategy might be a risk factor for the development of this injury, this study examined whether landing dynamics from drop jumps differed among healthy volleyball players (CON) and volleyball players with a jumper’s knee. The patients with jumper’s knee were divided into an asymptomatic group with a previous jumper’s knee (PJK) and a symptomatic group with a recent jumper’s knee (RJK). Methods: Inverse dynamics analyses were used to estimate lower extremity joint dynamics from 30, 50 and 70 cm drop jumps in the three groups (CON, n = 8; PJK, n = 7; RJK, n = 9). A univariate repeated measures analysis of variance was used to compare the different landing techniques. Results: Data analysis of the landing dynamics revealed that PJK showed higher knee angular velocities (p Conclusion: Where RJK used a landing technique to avoid high patellar tendon loading, PJK used a stiffer landing strategy, which may be a risk factor in the development of patellar tendinopathy.

/pdf/relationship-between-landing-strategy-and-patellar-i4f9dmw8f3.pdf

Relationship between landing strategy and patellar tendinopathy in volleyball

A body-fixed-sensor-based analysis of power during sit-to-stand movements.

Objective: The causal mechanism of the chronic sports injury patellar tendinopathy is not well understood. The aim of the present study was to compare ankle and knee joint dynamics during the performance of the volleyball spike jump between healthy volleyball players (n = 8) and asymptomatic volleyball players with previous patellar tendinopathy (n = 7). Design: Cross-sectional. Methods: Inverse dynamics were used to estimate ankle and knee joint dynamics. From these multiple biomechanical variables, a logistic regression was performed to estimate the probability of the presence or absence of previous patellar tendinopathy among the volleyball players studied. Results: Several biomechanical variables improved the prediction of the presence or absence of previous patellar tendinopathy. For landing, ankle plantar flexion at the time of touch-down, and knee range of motion during the first part of impact, and for take-off, loading rate of the knee extensor moment during the eccentric countermovement phase of take-off were predictive. As interaction effects, the presence or absence of previous patellar tendinopathy were correctly predicted by ankle and knee range of motion during the first part of impact, by loading rate of the knee extensor moment during the eccentric phases of take-off and landing, and by knee angular velocity during the eccentric phases of take-off and landing. Conclusion: Smaller joint flexion during the first part of landing impact, and higher rate of knee moment development during the eccentric phases of the spike-jump landing sequence, together with higher knee angular velocities, might be risk factors in the development of patellar tendinopathy in volleyball players.

https://bjsm.bmj.com/content/bjsports/42/6/483.full.pdf

Rob W Bisseling

Papers

Handling of impact forces in inverse dynamics

Relationship between landing strategy and patellar tendinopathy in volleyball

A body-fixed-sensor-based analysis of power during sit-to-stand movements.

Are the take-off and landing phase dynamics of the volleyball spike jump related to patellar tendinopathy?

Estimation of hip abduction moment based on body fixed sensors.