Special Issue "Wearable Sensors for Movement Analysis"
Deadline for manuscript submissions: 30 November 2020.
Interests: aging; gait; falls; freezing of gait; wearables; Parkinson’s disease; fractal physiology
Interests: neuromechanics; posture; gait; movement disorders; musculoskeletal disorders
Interests: balance; turning; home monitoring; Parkinson's disease; closed-loop systems for gait rehabilitation; mobile imaging
In 1964, H Gage reported on the use of an accelerometer to quantify the “smoothness” of walking. Thirteen years later, GL Smidt and colleagues took that work a step further and linked a 3-D accelerometer and footswitches to a PDP-12 to quantitatively characterize gait over a 7-meter pathway in patients with tibial-femoral knee implants. We have come a long way since those early studies. The PDP-12 is now “ancient history”. Today, with smartphones and cloud computing, wearables are used to evaluate movement, gait, balance, and mobility, in conventional research settings, as well as in our streets, in our homes, and even on ski slopes over extended periods of time, changing the landscape of wearables and opening up new opportunities for research and clinical applications. Other types of sensors, such as thermal and electrophysiological sensors, have been miniaturized and made wearable to integrate additional information on human movement. Whether they are used to provide new insights into physiology and patho-physiology; to assess the impact of a new drug, therapy or training; or to generate real-time feedback and interventions, wearables are becoming an increasingly ubiquitous tool for the study and treatment of movement and mobility.
As the journal Sensors celebrates its 20th year, and as we mark what may be the sixth decade of wearables for the study of movement, we invite you to take part in this celebration by submitting manuscripts for a Special Issue devoted to ‘’Wearables for Movement Analysis’’. Papers from a wide variety of perspectives that will help to advance the field are invited. For more information or questions about the fit of a potential manuscript, please e-mail us.
Prof. Jeffrey M. Hausdorff
Prof. Dr. Jaap van Dieen
Dr. Martina Mancini
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- inertial measurement units
- postural transitions
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Author：Silvia Del-Din; Rana Rehman and Lynn Rochester
Potential Title and Abstract
“Validation and comparison of novel lightweight template-based and machine learning algorithms for free-living gait detection.”
Background and Objectives:
Wearable-based free-living walking assessment methodologies require a valid and robust means of gait classification (GC) before further detailed gait quantification can take place . A suitable GC algorithm is one that can perform in as wide a variety of contexts as possible and accurately capture valid gait. Such versatility is vital in free-living conditions. This study aimed at validating a template-based method for free-living gait detection.
10 healthy adult subjects wore an accelerometer (AX3, Axivity) on the lower back and a synchronised GoPro camera on the trunk for two separate one-hour epochs . Videos were annotated and represented the gold standard for validation and performance quantification (sensitivity and specificity) of a lightweight versatile gait detection algorithm in various contexts. The algorithm uses the convolution of the vertical signal and a sinusoidal template with a frequency within the typical ambulation range (~2.5Hz). Applying a threshold to the resultant signal produced a binary signal for GC, Fig 1a. GC Performance indicators, ROC curves and associated AUC values were calculated as well as Spearman’s correlation (rho) for number of bouts.
The novel algorithm had good performance (accuracy 87%, sensitivity 72%, specificity 84%, AUC=0.78) and rho=0.8 (p<0.01) with slight positive bias. We contextualised GC performance: short indoors bouts were associated with lower sensitivity whereas shuffling, turning and other activities e.g. cycling were more often associated with lower specificity.
We validated a novel lightweight algorithm for free-living gait detection, the algorithm showed to perform very well in many contexts. Algorithm’s areas of improvement are related to activities in which periodicity is exhibited (e.g. cycling).
 Taborri.J et al. Gait partitioning methods: A systematic review. Sensors.2016:16(1):66
 Hickey.A et al. Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis. Physiological measurement. 2016:12;38(1)