Detecting self-harming activities with wearable devices

Malott, Levi; Bharti, Pratool; Hilbert, Nicholas; Gopalakrishna, Ganesh; Chellappan, Sriram

doi:10.1109/percomw.2015.7134105

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…This technology could be integrated with an automated alert system as well as a caring text intervention that automatically sends appropriate behavioral reminders to patients. Also, Malott and colleagues [35] have conducted preliminary testing of the use of wearable devices to detect self-harming behaviors of inpatients. The system, called A Self-Harm Activity Recognition Engine (SHARE), infers self-harming activities (e.g., wrist cutting) by making use of accelerometer data from a smart watch worn on patient's wrists.…”

Section: Emerging Capabilities With Mobile Technologiesmentioning

confidence: 99%

Mobile Health Technologies for Suicide Prevention: Feature Review and Recommendations for Use in Clinical Care

Luxton

June

Chalker

2015

Curr Treat Options Psych

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Keywords mHealth I Mobile applications I Apps I Technologies I Suicide I Self-directed violence Opinion statement Mobile devices, and the apps (software applications) that operate on them, have potential to help manage and prevent suicidal behavior by assisting with assessment of risk, providing educational and support information, and facilitating access to safety plans, crisis support, and coping tools. This clinician-friendly review provides an overview of the principal features of currently available mobile health apps that are specially designed for suicide prevention. The use of patient contact interventions (i.e., text messaging and email interventions) on mobile devices is also discussed. Recommendations for selecting and integrating these tools into patient care are provided, along with discussion of emerging capabilities and limitations including the lack of research regarding the clinical effectiveness of mobile health apps. The topics presented should be informative to individual practitioners as well as health care organizations that are considering adopting these technologies into their services.

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Section: Emerging Capabilities With Mobile Technologiesmentioning

confidence: 99%

Mobile Health Technologies for Suicide Prevention: Feature Review and Recommendations for Use in Clinical Care

Luxton

June

Chalker

2015

Curr Treat Options Psych

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“…In [182], tri-axial accelerometer, gyroscope, and magnetometer were employed using different classifiers such as least-square, Bayesian decision, and dynamic time warping (DTW) for HAR. As one of the interesting and important applications of HAR, Malott et al [172] introduced a self-harm activity recognition engine named SHARE, to infer self-harming activities from the wrist-worn accelerometer. They attached a smartphone on both wrists for activity recognition.…”

Section: Har With Wearable Inertial Sensorsmentioning

confidence: 99%

“…It was tested on 11 subjects performing a series of activities including self-harming and other activities. In contrast to SHARE [172], Watch-Dog [175] used Shimmer devices that could be comfortably worn as a watch or arm-band, which increase its practicality. A few other interesting applications used in wearable sensors based HAR include sleep monitoring [183], suggestions for sleep hygiene [184],…”

Section: Har With Wearable Inertial Sensorsmentioning

confidence: 99%

A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions

Yadav

Tiwari

Pandey

et al. 2021

Knowledge-Based Systems

161

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“…As for the use of wearable devices, Malott et al. [27] and Bharti et al. [28] used smart devices that can be worn on the wrist to infer the presence of self‐harm behaviours in subjects.…”

Section: Introductionmentioning

confidence: 99%

Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities

Yang

Luo

et al. 2023

IET Biometrics

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Non-suicide self-injury (NSSI) can be dangerous and difficult for guardians or caregivers to detect in time. NSSI refers to when people hurt themselves even though they have no wish to cause critical or long-lasting hurt. To timely identify and effectively prevent NSSI in order to reduce the suicide rates of patients with a potential suicide risk, the detection of NSSI based on the spatiotemporal features of indoor activities is proposed. Firstly, an NSSI behaviour dataset is provided, and it includes four categories that can be used for scientific research on NSSI evaluation. Secondly, an NSSI detection algorithm based on the spatiotemporal features of indoor activities (NssiDetection) is proposed. NssiDetection calculates the human bounding box by using an object detection model and employs a behaviour detection model to extract the temporal and spatial features of NSSI behaviour. Thirdly, the optimal combination schemes of NssiDetection is investigated by checking its performance with different behaviour detection methods and training strategies. Lastly, a case study is performed by implementing an NSSI behaviour detection prototype system. The prototype system has a recognition accuracy of 84.18% for NSSI actions with new backgrounds, persons, or camera angles. K E Y W O R D Sbehavioural sciences computing, computer vision, convolutional neural nets, feature extraction, object detection | INTRODUCTIONNon-suicidal self-injury (NSSI) [1] refers to the direct and repetitive intentional destruction of one's body in the absence of suicidal intent, and its typical behaviours include cutting, severe scratching, burning, biting, climbing and beating. More than 20% of adolescents and over 5% of adults commit self-injury [2,3]. NSSI is one of the emotional outlets for patients with depression, borderline personality disorder and other common mental diseases [4]. Particularly, patients with depression, accounting for 3.8% of the global population [5], are likely to commit NSSI according to the Global Health Data Exchange Platform.Usually, suicidal intent is absent in NSSI, but NSSI behaviours are meant for self-punishment or signal personal pain to the outside world, especially to important persons. NSSI is associated with potential suicide attempts, and this association implies that NSSI behaviours and their related psychology may already be on the same danger trajectory [6]. In addition, evidence shows that over time, raw emotions, such as anxiety, anger and sadness, along with additional feelings of guilt and shame will continue to exist and may even worsen [7].In conclusion, suicide and NSSI are not the same, but NSSI can be dangerous. Guardians or caregivers may have difficulty in determining when a person is hurting himself evenThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Detecting self-harming activities with wearable devices

Cited by 9 publications

References 15 publications

Mobile Health Technologies for Suicide Prevention: Feature Review and Recommendations for Use in Clinical Care

Mobile Health Technologies for Suicide Prevention: Feature Review and Recommendations for Use in Clinical Care

A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions

Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities

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