Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis

Rivolta, Massimo W.; Aktaruzzaman, Md.; Rizzo, Giovanna; Lafortuna, Claudio L.; Ferrarin, Maurizio; Bovi, G.; Bonardi, Daniela; Caspani, Andrea Mario; Sassi, Roberto

doi:10.1016/j.artmed.2018.08.005

Cited by 52 publications

(93 citation statements)

References 33 publications

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“…Rivolta et al [74] 2019 Investigate the use of wearable accelerometer to evaluate the fall risk determined by the Tinetti clinical scale.…”

Section: Reference Year Objectivementioning

confidence: 99%

“…Machine learning has been playing an important role in gait, balance, and RoM analysis in recent years. Machine learning techniques can be used to quantify gait, balance, and ROM parameters [35,36,62,66,70], distinguish between populations and conditions [45,46,51,61], and estimate assessments scores [74,75]. The techniques used in the literature showed the efficiency of machine learning to reduce and create gait, balance, and RoM parameters.…”

Section: Machine Learning In Gait Balance and Rommentioning

confidence: 99%

“…Additionally, studies in this review that used machine learning techniques showed the highest accuracy, 88-99%, for both parameter quantification and population/condition classification. Moreover, recent studies showed that gait and balance assessments scores, such as Berg Balance Score and Tinetti Test Scores, can be estimated by using features extracted from wearable devices and machine learning models [74,75].…”

Section: Machine Learning In Gait Balance and Rommentioning

confidence: 99%

“…Additionally, most of the research builds classification models focused on a binary classification, healthy versus non-healthy, limiting the adaptability and reliability for different targeted population. Also, the selection of features might be constrained by the number of subjects that participate in the study [74]. At last, when using models based on activities from gold standard assessments, the person needs to perform all the activities in which the model was constructed.…”

Section: Machine Learning In Gait Balance and Rommentioning

confidence: 99%

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Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

2019

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More than 8.6 million people suffer from neurological disorders that affect their gait and balance. Physical therapists provide interventions to improve patient’s functional outcomes, yet balance and gait are often evaluated in a subjective and observational manner. The use of quantitative methods allows for assessment and tracking of patient progress during and after rehabilitation or for early diagnosis of movement disorders. This paper surveys the state-of-the-art in wearable sensor technology in gait, balance, and range of motion research. It serves as a point of reference for future research, describing current solutions and challenges in the field. A two-level taxonomy of rehabilitation assessment is introduced with evaluation metrics and common algorithms utilized in wearable sensor systems.

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“…Rivolta et al [74] 2019 Investigate the use of wearable accelerometer to evaluate the fall risk determined by the Tinetti clinical scale.…”

Section: Reference Year Objectivementioning

confidence: 99%

Section: Machine Learning In Gait Balance and Rommentioning

confidence: 99%

Section: Machine Learning In Gait Balance and Rommentioning

confidence: 99%

Section: Machine Learning In Gait Balance and Rommentioning

confidence: 99%

See 2 more Smart Citations

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

2019

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“…Benoit [28] showed that several classification algorithms were able to discriminate people from two interest groups, fallen and non-fallen hospitalized, recording limb accelerations during clinical trials with a network of accelerometers distributed throughout the body. Rivolta [29] Several patients and volunteers underwent a full test of Tinetti while wearing a triaxial chest accelerometer. Tinetti scores were evaluated by medical experts and subjects with a score of ≤18 were considered to be at high risk.…”

Section: Technologiesmentioning

confidence: 99%

Fall risk in the aging population: fall prevention using smartphones technology and multiscale sample entropy

et al. 2020

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Falls are an important aspect of older people's health because they trigger major injuries and even death in one-third of fallen patients, making them a major public health problem. Given the risk of physical and psychological injury, if serious injuries occur as a result of a fall, prevention is an important consideration in today's health care landscape, where the population is predominantly adult worldwide. This paper presents the applicability of a simple technique of analysis of gait signals captured by mobile devices with the objective to the generation of early warnings on the risk of falls in older adults, which correlates with subjective scales. The technique is tested in a population of patients showing results of the significant risk of falls in patients that the subjective scales could not detect, demonstrating that mobile devices and signal processing can become important tools in the service of elderly care in fall risk prevention.

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Technologies to Prevent Falls and Their Consequences

Bohlke,

Suri,

Sejdcic

et al. 2023

Practical Issues in Geriatrics

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Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis

Cited by 52 publications

References 33 publications

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

Fall risk in the aging population: fall prevention using smartphones technology and multiscale sample entropy

Technologies to Prevent Falls and Their Consequences

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