Evaluation of Kinect2 based balance measurement

Lv, Zhihan; Penades, Vicente; Blasco, Sonia Val; Chirivella, Javier; Gagliardo, Pablo

doi:10.1016/j.neucom.2015.12.128

Cited by 17 publications

(10 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its measurement errors have also been carefully investigated [39][40][41][42][43]. These results clearly support the use of the Kinect sensor for the assessment of gait and balance performance [44][45][46][47][48][49][50][51][52][53][54][55][56]. It should be noted that many similar RGB-depth (RGB-D) sensor devices are already available.…”

Section: Introductionsupporting

confidence: 53%

Study of Postural Stability Features by Using Kinect Depth Sensors to Assess Body Joint Coordination Patterns

Liu

Lee

Chen

et al. 2020

Sensors

View full text Add to dashboard Cite

A stable posture requires the coordination of multiple joints of the body. This coordination of the multiple joints of the human body to maintain a stable posture is a subject of research. The number of degrees of freedom (DOFs) of the human motor system is considerably larger than the DOFs required for posture balance. The manner of managing this redundancy by the central nervous system remains unclear. To understand this phenomenon, in this study, three local inter-joint coordination pattern (IJCP) features were introduced to characterize the strength, changing velocity, and complexity of the inter-joint couplings by computing the correlation coefficients between joint velocity signal pairs. In addition, for quantifying the complexity of IJCPs from a global perspective, another set of IJCP features was introduced by performing principal component analysis on all joint velocity signals. A Microsoft Kinect depth sensor was used to acquire the motion of 15 joints of the body. The efficacy of the proposed features was tested using the captured motions of two age groups (18–24 and 65–73 years) when standing still. With regard to the redundant DOFs of the joints of the body, the experimental results suggested that an inter-joint coordination strategy intermediate to that of the two extreme coordination modes of total joint dependence and independence is used by the body. In addition, comparative statistical results of the proposed features proved that aging increases the coupling strength, decreases the changing velocity, and reduces the complexity of the IJCPs. These results also suggested that with aging, the balance strategy tends to be more joint dependent. Because of the simplicity of the proposed features and the affordability of the easy-to-use Kinect depth sensor, such an assembly can be used to collect large amounts of data to explore the potential of the proposed features in assessing the performance of the human balance control system.

show abstract

Section: Introductionsupporting

confidence: 53%

Study of Postural Stability Features by Using Kinect Depth Sensors to Assess Body Joint Coordination Patterns

Liu

Lee

Chen

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Bertolotti et al [ 35 ] Body Sway and Trunk Kinematics Data Customized Inertial Sensor Unit (Gyro, and, Accelerometer) Selected Tasks Performed from Tinetti Tests, Balance Evaluation Systems Test and Berg Balance None Validating the Use of Newly Developed Unit Against Balance Board and Marker-Based System. 5 Z. Lv et al [ 40 ] CoM Kinect Double Leg Stance, sttar excursion balance Test None Validating Kinect V2 for Balance Measurement 6 A. Nalci et al [ 79 ] Variation in pixel events Camera UPST None Developed Vision Based Model To Assess Balance …”

Section: Resultsmentioning

confidence: 99%

“…In a related approach, Lv and colleagues [40] estimated the balance parameters using a Kinect v2 in conjunction with a Wii balance board. Various view angles of the Kinect sensors were explored.…”

Section: Balance Analysis Using Sensormentioning

confidence: 99%

“…The vision sensor based approaches are widely reported in the literature to address risk of fall assessments, balance assessments and postural assessments [ 38 – 40 ]; yet such sensors are rarely used to assess frailty. However, vision sensors are preferred choice when patients or subjects have aversion to body-worn sensors while performing the tasks [ 32 ].…”

Section: Quantitative Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of frailty: a survey of quantitative and clinical methods

Panhwar

Naghdy

et al. 2019

BMC biomed eng

View full text Add to dashboard Cite

Background: Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty. Results: The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty. Conclusion: Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

show abstract

“…Several studies have focused on its measurement errors [12]. These results support the use of the Kinect sensor to assess walking and balance performance [13][14][15][16][17]. It is already mentioned the availability of some similar RGB depth sensors (RGB-D).…”

Section: Introductionmentioning

confidence: 64%

Improvement of Body Movements and Stability of Blind or Visually Impaired Adults by Physical Activity using Kinect V2

Bouri¹,

Khalfallah²,

Bouhlel³

2020

IJACSA

View full text Add to dashboard Cite

People who are blind or low vision need to follow activities routines for their mental and physical health to minimize the risk of suffering from bleeding in articulation but they have problems due to difficulties and inaccessibility of displacement. This paper introduces and evaluate a set of exercises to improve the bodily movement and stability using body tracking by Microsoft Kinect V2 and audio feedback. These exercises are composed of a sequence of different postures, has an audio feedback personalized to help people to understand each gestures and can correct them if it is not correct, and generates a summary graph to evaluate the success rate of exercises. To obtain the 3D joint coordinates from the depth sensor, we used the SDK V2.0 of the Microsoft Kinect. We use these coordinates to calculate the distances and angles between joints of interest firstly to position the user in the area field of the Kinect sensor, evaluate the different postures of movements of knees, elbows and shoulders, and detect the body balance if he is leaning and in which direction to avoid falling. These physical exercises have been evaluated to improve feasibility and feedback with persons who are blind or low vision.

show abstract

Evaluation of Kinect2 based balance measurement

Cited by 17 publications

References 70 publications

Study of Postural Stability Features by Using Kinect Depth Sensors to Assess Body Joint Coordination Patterns

Study of Postural Stability Features by Using Kinect Depth Sensors to Assess Body Joint Coordination Patterns

Assessment of frailty: a survey of quantitative and clinical methods

Improvement of Body Movements and Stability of Blind or Visually Impaired Adults by Physical Activity using Kinect V2

Contact Info

Product

Resources

About