IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring

Young, Fraser; Mason, Rachel; Morris, Rosie; Stuart, Samuel; Godfrey, Alan

doi:10.3390/s23084100

Cited by 9 publications

(3 citation statements)

References 185 publications

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“…Micro-electromechanical-systems (MEMS) technology makes IMUs portable and wearable [178]. Wearable IMUs are widely used in gait parameters [179,180], Parkinsonian and seizure attack detection [181], monitoring exercise [181], postural feedback [182], human movements analysis [183], HMI [184], and so on.…”

Section: Imumentioning

confidence: 99%

Recent Advances in Wearable Healthcare Devices: From Material to Application

Luo,

Tan,

Wen

2024

Bioengineering

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In recent years, the proliferation of wearable healthcare devices has marked a revolutionary shift in the personal health monitoring and management paradigm. These devices, ranging from fitness trackers to advanced biosensors, have not only made healthcare more accessible, but have also transformed the way individuals engage with their health data. By continuously monitoring health signs, from physical-based to biochemical-based such as heart rate and blood glucose levels, wearable technology offers insights into human health, enabling a proactive rather than a reactive approach to healthcare. This shift towards personalized health monitoring empowers individuals with the knowledge and tools to make informed decisions about their lifestyle and medical care, potentially leading to the earlier detection of health issues and more tailored treatment plans. This review presents the fabrication methods of flexible wearable healthcare devices and their applications in medical care. The potential challenges and future prospectives are also discussed.

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Section: Imumentioning

confidence: 99%

Recent Advances in Wearable Healthcare Devices: From Material to Application

Luo,

Tan,

Wen

2024

Bioengineering

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“…Often, IMUs have routine connectivity capabilities through the Internet of Things (IoT), where they can routinely integrate with other systems, offering a streamlined and more rounded experience for clinicians and patients alike [ 24 ]. IMUs are also capable of real-time sensing.…”

Section: Assessing Gaitmentioning

confidence: 99%

Sonification for Personalised Gait Intervention

Wall,

McMeekin,

Walker

et al. 2023

Sensors

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Mobility challenges threaten physical independence and good quality of life. Often, mobility can be improved through gait rehabilitation and specifically the use of cueing through prescribed auditory, visual, and/or tactile cues. Each has shown use to rectify abnormal gait patterns, improving mobility. Yet, a limitation remains, i.e., long-term engagement with cueing modalities. A paradigm shift towards personalised cueing approaches, considering an individual’s unique physiological condition, may bring a contemporary approach to ensure longitudinal and continuous engagement. Sonification could be a useful auditory cueing technique when integrated within personalised approaches to gait rehabilitation systems. Previously, sonification demonstrated encouraging results, notably in reducing freezing-of-gait, mitigating spatial variability, and bolstering gait consistency in people with Parkinson’s disease (PD). Specifically, sonification through the manipulation of acoustic features paired with the application of advanced audio processing techniques (e.g., time-stretching) enable auditory cueing interventions to be tailored and enhanced. These methods used in conjunction optimize gait characteristics and subsequently improve mobility, enhancing the effectiveness of the intervention. The aim of this narrative review is to further understand and unlock the potential of sonification as a pivotal tool in auditory cueing for gait rehabilitation, while highlighting that continued clinical research is needed to ensure comfort and desirability of use.

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“…Smartphones, with their wireless connection, inertial sensors, and cameras, have greatly impacted digital health and gait analysis studies. Smartphones are currently providing physiological assessment and data entry/collection capabilities through several sensing modalities, which are accessible via apps [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestrian Walking Behavior: A Case Study at the University of Moratuwa

Sawandi,

Jayasinghe,

Retscher

2024

Sensors

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The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such pedestrian-friendly walking environments. Identifying locations and routes with high pedestrian concentration is critical for improving pedestrian-friendly walking environments. This paper presents a quantitative method to map pedestrian walking behavior by utilizing real-time data from mobile phone sensors, focusing on the University of Moratuwa, Sri Lanka, as a case study. This holistic method integrates new urban data, such as location-based service (LBS) positioning data, and data clustering with unsupervised machine learning techniques. This study focused on the following three criteria for quantifying walking behavior: walking speed, walking time, and walking direction inside the experimental research context. A novel signal processing method has been used to evaluate speed signals, resulting in the identification of 622 speed clusters using K-means clustering techniques during specific morning and evening hours. This project uses mobile GPS signals and machine learning algorithms to track and classify pedestrian walking activity in crucial sites and routes, potentially improving urban walking through mapping.

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IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring

Cited by 9 publications

References 185 publications

Recent Advances in Wearable Healthcare Devices: From Material to Application

Recent Advances in Wearable Healthcare Devices: From Material to Application

Sonification for Personalised Gait Intervention

Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestrian Walking Behavior: A Case Study at the University of Moratuwa

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