Motion initiated music ensemble with sensors for motor rehabilitation

Murad, Dania; Ye, Fenyi; Barone, Michael D.; Wang, Ye

doi:10.1109/icot.2017.8336095

Cited by 3 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the included studies has utilized a wrist wearable system for assessment purposes on patients with distal radius fractures [30]. Three other studies on arthritis and motor impairment patients have used wrist-mounted wearable sensors [31][32][33]. Five studies focused on hand and finger diseases such as hand arthritis [34,35] and carpal tunnel syndrome [36,37], applied wearable sensors (such as IMUs, and piezoresistive sensors) that were either embedded in a glove or attached with straps.…”

Section: Clinical Population Studiesmentioning

confidence: 99%

“…One of the critical aspects is finding the rate of studies aimed to use wearable sensor platforms for either rehabilitation or treatment applications or increasing the quality of remote treatment sessions. In this regard, 11 studies in the clinical population studies category (35% of studies in the category) [26,27,29,32,33,36,37,40,[42][43][44], three studies in the workers' population studies category (10% of studies in the category) [62,80,85], and two studies in the general wearable studies category (11% of studies in the category) have focused on treatment applications [87,98].…”

Section: General Wearable Design/performance Studiesmentioning

confidence: 99%

“…Designing or using a smartphone application for monitoring or displaying the obtained data in several studies was another significant finding of this review. In this aspect, ten studies have either used or developed a smartphone app in all three categories [26,27,31,33,62,65,77,78,89,98]. Considering the remote data collection method in some studies, this ability can facilitate the supervision and connection of researchers, clinicians, and patients.…”

Section: General Wearable Design/performance Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Applications of wearable sensors in upper extremity MSK conditions: a scoping review

Zadeh,

MacDermid,

Johnson

et al. 2023

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Purpose This scoping review uniquely aims to map the current state of the literature on the applications of wearable sensors in people with or at risk of developing upper extremity musculoskeletal (UE-MSK) conditions, considering that MSK conditions or disorders have the highest rate of prevalence among other types of conditions or disorders that contribute to the need for rehabilitation services. Materials and methods The preferred reporting items for systematic reviews and meta-analysis (PRISMA) extension for scoping reviews guideline was followed in this scoping review. Two independent authors conducted a systematic search of four databases, including PubMed, Embase, Scopus, and IEEEXplore. We included studies that have applied wearable sensors on people with or at risk of developing UE-MSK condition published after 2010. We extracted study designs, aims, number of participants, sensor placement locations, sensor types, and number, and outcome(s) of interest from the included studies. The overall findings of our scoping review are presented in tables and diagrams to map an overview of the existing applications. Results The final review encompassed 80 studies categorized into clinical population (31 studies), workers’ population (31 studies), and general wearable design/performance studies (18 studies). Most were observational, with 2 RCTs in workers’ studies. Clinical studies focused on UE-MSK conditions like rotator cuff tear and arthritis. Workers’ studies involved industrial workers, surgeons, farmers, and at-risk healthy individuals. Wearable sensors were utilized for objective motion assessment, home-based rehabilitation monitoring, daily activity recording, physical risk characterization, and ergonomic assessments. IMU sensors were prevalent in designs (84%), with a minority including sEMG sensors (16%). Assessment applications dominated (80%), while treatment-focused studies constituted 20%. Home-based applicability was noted in 21% of the studies. Conclusion Wearable sensor technologies have been increasingly applied to the health care field. These applications include clinical assessments, home-based treatments of MSK disorders, and monitoring of workers’ population in non-standardized areas such as work environments. Assessment-focused studies predominate over treatment studies. Additionally, wearable sensor designs predominantly use IMU sensors, with a subset of studies incorporating sEMG and other sensor types in wearable platforms to capture muscle activity and inertial data for the assessment or rehabilitation of MSK conditions.

show abstract

Section: Clinical Population Studiesmentioning

confidence: 99%

Section: General Wearable Design/performance Studiesmentioning

confidence: 99%

Section: General Wearable Design/performance Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Applications of wearable sensors in upper extremity MSK conditions: a scoping review

Zadeh,

MacDermid,

Johnson

et al. 2023

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

“…The gamified approaches which have been developed involves adapted guitar for upper limbs rehabilitation for stroke-affected individuals (Dechenaud et al, 2019). It also includes gesture recognition tools for motor rehabilitation (Murad et al, 2017). Researches have also investigated and presented good results for therapies based on vibro-acoustics.…”

Section: Games Virtual Reality Affective Robotics and Therapiesmentioning

confidence: 99%

“…Finally, it is important to point out that only 9 studies, among the 33 studies mentioned in this subsection, used Artificial Intelligence techniques (T. Aranha et al, 2017;Song et al, 2019;Murad et al, 2017;Cavallo et al, 2020;Teo & Chia, 2018;Lyu & Yuan, 2020;Sra et al, 2017). The mostly used techniques were Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN), Decision Trees and K-Nearest Neighbor (KNN).…”

Section: Games Virtual Reality Affective Robotics and Therapiesmentioning

confidence: 99%

Affective computing in the context of music therapy: a systematic review

Santana

Lima

Torcate

et al. 2021

RSD

View full text Add to dashboard Cite

Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

show abstract