Extracting Gait Velocity and Stride Length from Surrounding Radio Signals

Hsu, Chen-Yang; Liu, Yuchen; Kabelac, Zachary; Hristov, Rumen; Katabi, Dina; Liu, Christine

doi:10.1145/3025453.3025937

Cited by 103 publications

(75 citation statements)

References 34 publications

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“…In terms of data processing, additional features can be added to the pool of those considered (e.g. "jerk" [41] or wavelet-based [19] features for wearable sensors, or additional representation domains for the radar data [42]), and additional feature selection methods and metrics for information fusion investigated. The application of deep learning methods may also be considered, in particular the challenge of using deep networks with small amount of experimental data available, for example through transfer learning approaches or through the generation of suitable simulation data.…”

Section: Discussionmentioning

confidence: 99%

“…Radar sensors are attractive for their contactless and non-cooperative monitoring capabilities, no reliance on users' compliance, and detection/classification ranges of tens of meters. They are expected to be perceived as more privacy-oriented, as no plain images of the monitored people are recorded [19]. The simultaneous use of heterogeneous sensors allows overcoming the performance limitations of each sensor considered individually, or possible malfunctions of one of them (for example "drift problem" inherent to accelerometers [13], or classification accuracy reduction for radar sensors relying on Doppler-based classification for tangential views of the person monitored [17]).…”

Section: Introductionmentioning

confidence: 99%

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A Multisensory Approach for Remote Health Monitoring of Older People

Shrestha

Heidari

et al. 2018

IEEE J. Electromagn. RF Microw. Med. Biol.

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Growing life expectancy and increasing incidence of multiple chronic health conditions are significant societal challenges. Different technologies have been proposed to address these issues, to detect critical events such as stroke or falls, and to monitor automatically human activities for health condition inference and anomalies detection. This paper aims to investigate two types of sensing technologies proposed for assisted living: wearable and radar sensors. First, different feature selection methods are validated and compared in terms of accuracy and computational loads. Then, information fusion is applied to enhance activity classification accuracy combining the two sensors. Improvements in classification accuracy of approximately 12% using feature level fusion is achieved with both Support Vector Machine and K Nearest Neighbor classifiers. Decision-level fusion schemes are also investigated, yielding classification accuracy in the order of 97-98%.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multisensory Approach for Remote Health Monitoring of Older People

Shrestha

Heidari

et al. 2018

IEEE J. Electromagn. RF Microw. Med. Biol.

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“…Furthermore, these devices may result in further agitation for subjects with mood disorders [36]. Recently, gait characteristics have been extracted from radio signals that tract subjects and provide 3D information even through walls, the so-called 'invisible devices' [96].…”

Section: A Gait Capture Methodologymentioning

confidence: 99%

From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology

Deligianni

Guo

Yang

2019

IEEE J. Biomed. Health Inform.

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Mood disorders affect more than 300 million people worldwide and can cause devastating consequences. Elderly people and patients with neurological conditions are particularly susceptible to depression. Gait and body movements can be affected by mood disorders, and thus they can be used as a surrogate sign, as well as an objective index for pervasive monitoring of emotion and mood disorders in daily life. Here we review evidence that demonstrates the relationship between gait, emotions and mood disorders, highlighting the potential of a multimodal approach that couples gait data with physiological signals and home-based monitoring for early detection and management of mood disorders. This could enhance self-awareness, enable the development of objective biomarkers that identify high risk subjects and promote subject-specific treatment.

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“…The device has been shown to accurately measure an individual’s position to within 15 cm [6, 8] and gait speed to within 0.025 m/s [7]. Emerald can be installed in less than 1 h and hangs on the wall like a picture (Fig.…”

Section: Methodsmentioning

confidence: 99%

Passive Monitoring at Home: A Pilot Study in Parkinson Disease

et al. 2019

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We conducted a pilot study using a passive radio-wave-based home monitor in individuals with Parkinson disease (PD) with a focus on gait, home activity, and time in bed. We enrolled 7 ambulatory individuals to have the device installed in the bedroom of their homes over 8 weeks and performed standard PD assessments at baseline. We evaluated the ability of the device to objectively measure gait and time in bed and to generate novel visualizations of home activity. We captured 353 days of monitoring. Mean gait speed (0.39–0.78 m/s), time in bed per day (4.4–12.1 h), and number (1.4–5.9) and duration (15.0–49.8 min) of nightly awakenings varied substantially across and within individuals. Derived gait speed correlated well with the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale total (r = –0.88, p = 0.009) and motor sub-score (r = –0.95, p = 0.001). Six of the seven participants agreed that their activity was typical and indicated a willingness to continue monitoring. This technology provided promising new insights into the home activities of those with PD and may be broadly applicable to other chronic conditions.

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Extracting Gait Velocity and Stride Length from Surrounding Radio Signals

Cited by 103 publications

References 34 publications

A Multisensory Approach for Remote Health Monitoring of Older People

A Multisensory Approach for Remote Health Monitoring of Older People

From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology

Passive Monitoring at Home: A Pilot Study in Parkinson Disease

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