Wearable Inertial Gait Algorithms: Impact of Wear Location and Environment in Healthy and Parkinson’s Populations

Çelik, Yunus; Stuart, Samuel; Woo, Wai Lok; Godfrey, Alan

doi:10.3390/s21196476

Cited by 18 publications

(11 citation statements)

References 50 publications

(93 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The field of mobility-based gait analysis is readily adopting the use of IMUs due to their more affordable cost (compared to e.g., instrumented walkways) and unobtrusiveness while capturing data in a range of environments [ 17 ]. Currently, there are a plethora of IMU devices used for mobility-based gait analysis [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Moore

Stuart

McMeekin

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment.

show abstract

Section: Methodsmentioning

confidence: 99%

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Moore

Stuart

McMeekin

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…These applications can be integrated into different areas, such as the health area [22]. One of the applications in this context is the identification of a neurological disorder in the user, such as Parkinson's [23].…”

Section: Applications Of Wearable Systems To Harmentioning

confidence: 99%

Towards novel smart wearable sensors to classify subject-specific human walking activities

Silva

Amorim

Lazaroni³

et al. 2022

Anais Estendidos Do XII Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2022)

View full text Add to dashboard Cite

In this century, smart devices are increasingly present in our lives, such as at work, sports, or household chores. In this context, we have wearable devices that can help people with health monitoring or physical performance in sports activities. With the integration of artificial intelligence (AI), these wearable devices can identify injuries in athletes or care for the elderly in rehabilitation from human activity recognition (HAR). AI techniques are commonly applied for pattern recognition, such as image classification or HAR. In this context, we seek to develop a smart wearable device to recognize walking activities. In order to improve the identification of these tasks through AI algorithms, we propose the fusion of data between four sensors called SPUs. Each SPU has NodeMCU ESP-32 and BNO080 IMU hardware in its architecture. The data from these hardware provides information in high precision. A zero W raspberry pi collected this information. After extracting and manipulating this data, we trained a deep learning model. The model accuracy was higher than 92% reaching an overall accuracy of 97%. Therefore, the smart wearable device showed a new tool for recognizing walking activity, which could be applied in the future to recognize more complex tasks.

show abstract

“…Spatial characteristics used another validated algorithm 21 , involving a high pass filter and double integration on the signal before using where l is IMU height from the ground and h is the absolute difference between the minima and maxima of the integration of the signal. The general methodology has been used for many years and is generally perceived as a pragmatic approach to habitual gait assessment 22 , 23 .…”

Section: Introductionmentioning

confidence: 99%

Contextualizing remote fall risk: Video data capture and implementing ethical AI

Moore,

McMeekin,

Parkes

et al. 2024

npj Digit. Med.

Self Cite

View full text Add to dashboard Cite

Wearable inertial measurement units (IMUs) are being used to quantify gait characteristics that are associated with increased fall risk, but the current limitation is the lack of contextual information that would clarify IMU data. Use of wearable video-based cameras would provide a comprehensive understanding of an individual’s habitual fall risk, adding context to clarify abnormal IMU data. Generally, there is taboo when suggesting the use of wearable cameras to capture real-world video, clinical and patient apprehension due to ethical and privacy concerns. This perspective proposes that routine use of wearable cameras could be realized within digital medicine through AI-based computer vision models to obfuscate/blur/shade sensitive information while preserving helpful contextual information for a comprehensive patient assessment. Specifically, no person sees the raw video data to understand context, rather AI interprets the raw video data first to blur sensitive objects and uphold privacy. That may be more routinely achieved than one imagines as contemporary resources exist. Here, to showcase/display the potential an exemplar model is suggested via off-the-shelf methods to detect and blur sensitive objects (e.g., people) with an accuracy of 88%. Here, the benefit of the proposed approach includes a more comprehensive understanding of an individual’s free-living fall risk (from free-living IMU-based gait) without compromising privacy. More generally, the video and AI approach could be used beyond fall risk to better inform habitual experiences and challenges across a range of clinical cohorts. Medicine is becoming more receptive to wearables as a helpful toolbox, camera-based devices should be plausible instruments.

show abstract

Wearable Inertial Gait Algorithms: Impact of Wear Location and Environment in Healthy and Parkinson’s Populations

Cited by 18 publications

References 50 publications

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Towards novel smart wearable sensors to classify subject-specific human walking activities

Contextualizing remote fall risk: Video data capture and implementing ethical AI

Contact Info

Product

Resources

About