This would seem to be incredibly important to monitor stroke survivors with and set this up so it immediately(text message?) tells them when they are doing these transitions wrong. But alas, nothing will be done with this since there is NO STROKE LEADERSHIP to go to and get this accomplished.
Postural transitions detection and characterization in healthy and patient populations using a single waist sensor
Abstract
Background
Sit-to-stand and stand-to-sit transitions are frequent daily functional tasks indicative of muscle power and balance performance. Monitoring these postural transitions with inertial sensors provides an objective tool to assess mobility in both the laboratory and home environment. While the measurement depends on the sensor location, the clinical and everyday use requires high compliance and subject adherence. The objective of this study was to propose a sit-to-stand and stand-to-sit transition detection algorithm that works independently of the sensor location.Methods
For a location-independent algorithm, the vertical acceleration of the lower back in the global frame was used to detect the postural transitions in daily activities. The detection performance of the algorithm was validated against video observations. To investigate the effect of the location on the kinematic parameters, these parameters were extracted during a five-time sit-to-stand test and were compared for different locations of the sensor on the trunk and lower back.Results
The proposed detection method demonstrates high accuracy in different populations with a mean positive predictive value (and mean sensitivity) of 98% (95%) for healthy individuals and 89% (89%) for participants with diseases.Conclusions
The sensor location around the waist did not affect the performance of the algorithm in detecting the sit-to-stand and stand-to-sit transitions. However, regarding the accuracy of the kinematic parameters, the sensors located on the sternum and L5 vertebrae demonstrated the highest reliability.Background
Being
able to maintain balance during movements is a prerequisite for an
independent life. The inability to do so can lead to an increased risk
of falls and consequently a dependent and inactive life [1–3]. Balance disorders can lead to problems with postural transitions (PTs), such as the sit-to-stand movements [4]. These challenging PTs require complicated coordination of lower and upper limbs [5] and frequently occur during daily living activities [6, 7]. As the sit-to-stand transitions are indicative of lower limb muscle strength and balance control [6, 8, 9], quantifying these movements is key to understand the underlying problem of balance disorders.
Clinicians conventionally assess the sit-to-stand transitions by either diaries [10] and questionnaires [11, 12] or functional tests. Standardized assessment tools can provide here valuable additional information.
The five-time sit-to-stand (5xSTS) test which measures the time to perform five sit-to-stand transitions [13, 14] and thirty-second chair-rise (30SCT) test which includes the numbers of sit-to-stands that can be performed within thirty seconds [8, 15] are standardized functional tests used in clinical routine to assess the ability to perform, and the quality of transitions. Although these methods have been proven to display discriminative properties for balance disorders [16], subtle differences that may provide further relevant information about the movement are not detectable with these tests [17].
For instance, during sit-to-stand transitions, maximum angular velocity has been shown to be associated with inadequate momentum generation and consequently, the success of the PT [18, 19]. Moreover, duration of each phase of sit-to-stand transitions changes between young and old adults [20] and between older adults with a low or a high risk of fall [21]. Peak power of transition has been reported to be associated with muscle power and strength [22, 23]. Therefore, instrumenting these functional tests and extracting meaningful parameters can provide a more in-depth and precise analysis. Sit-to-stand transitions have been studied with optical motion trackers [24] and force plates [25]. Although these methods provide very detailed and granular information about the movements, they are limited to the laboratory environment [7, 17].
The laboratory setting can only assess the performance of the participants in the confined environment (e.g. in-clinic) while individuals demonstrate different behavior in real-life daily activities [26, 27]. For example, sit-to-stand duration has been shown to be higher during daily activities compared to the functional test performed in the clinic in older adults and in patients with idiopathic Parkinson’s disease (IPS) [28]. Thus, it is important to develop methods that can also be used in domestic environments.
Inertial sensors can be applied in almost every environment. Moreover, they have been already used to instrument the 5xSTS [29] or the 30SCT [30] tests. Kinematic parameters extracted from such instrumented assessments have been shown to have greater clinical relevance than the conventional clinical approach [31, 32]. Wearable sensors have provided an objective tool to evaluate PTs during daily activities as well. Barometric pressure sensor within the pendant device was used as a complementary source of data to detect the PTs [3, 33]; however, due to the pressure changes in outdoor environments, the use of the barometric sensor can adversely affect the detection accuracy. For instance, in [3], the sensitivity of the sit-to-stand detection was decreased by 25% in outdoor environments.
There are some studies on monitoring sit-to-stand transitions with a single inertial sensor on either the sternum or on the lower back. In reference [34], the gyroscope and accelerometer signal along with a discrete wavelet transform have been used to obtain the trunk angle and consequently to detect the PTs. A simpler sensor setup with only a tri-axial accelerometer was used in [35]. In this study, the tilt angle of the trunk was estimated by the scalar product of the accelerometer data and gravity vector obtained during a static calibration at the beginning of each measurement. These studies were validated under very controlled conditions that involved sit-to-stand and stand-to-sit movements with a few other activities. More daily activities were included in the measurement protocol used by [36] and to reduce the false positive trunk movements, fuzzy rules have been employed to improve the accuracy of detection based on the previous or next activity. The performance of the PT detection was further improved by employing a template matching technique with dynamic time warping method in [37]. However, the performance of the detection algorithm was still unsatisfying with a positive predictive value and sensitivity of 22% and 50%, respectively. In another study, with a single inertial sensor on the waist, the candidates of the PTs were first detected by detecting the peaks of the tilt angle of the lower back. These were filtered out by double integrating the vertical acceleration and calculating the elevation change of the lower back [38].
The drawback of all of these studies [21, 34–40] is that they require the sensor to be attached to a specific and fixed location of the body, which is difficult to maintain during daily activities and may not be achievable by patients themselves without a trained operator, thus limiting its broaden applicability in clinical setting.
This issue has been partially solved through using the signal vector magnitude which is the Euclidean norm of the accelerometer signal [41, 42]. The choice of various wavelets and scale approximations were studied in [42] to detect the PTs in a large group of healthy younger and older adults. However, in both of these studies, no method was suggested to distinguish true PTs from movements that can have similar wavelets to PTs. Their algorithms have been validated in measurements involving only sit-to-stand and stand-to-sit movements with rest periods in-between.
To this end, an algorithm which is robust to sensor placement changes and validated in a range of daily activities is desirable. Furthermore, little is known about the transferability of algorithms developed within a certain cohort, to other cohorts (e.g., with different and without diseases). The goal of this study was therefore to evaluate the performance of a new PT detection algorithm in healthy individuals and patients with different diseases that were all equipped with an inertial sensor on different locations around the waist and on the trunk. The new algorithm was validated in both laboratory and daily activity settings. Finally, the effect of the sensors location on the detection performance and extracted parameters was evaluated.
Clinicians conventionally assess the sit-to-stand transitions by either diaries [10] and questionnaires [11, 12] or functional tests. Standardized assessment tools can provide here valuable additional information.
The five-time sit-to-stand (5xSTS) test which measures the time to perform five sit-to-stand transitions [13, 14] and thirty-second chair-rise (30SCT) test which includes the numbers of sit-to-stands that can be performed within thirty seconds [8, 15] are standardized functional tests used in clinical routine to assess the ability to perform, and the quality of transitions. Although these methods have been proven to display discriminative properties for balance disorders [16], subtle differences that may provide further relevant information about the movement are not detectable with these tests [17].
For instance, during sit-to-stand transitions, maximum angular velocity has been shown to be associated with inadequate momentum generation and consequently, the success of the PT [18, 19]. Moreover, duration of each phase of sit-to-stand transitions changes between young and old adults [20] and between older adults with a low or a high risk of fall [21]. Peak power of transition has been reported to be associated with muscle power and strength [22, 23]. Therefore, instrumenting these functional tests and extracting meaningful parameters can provide a more in-depth and precise analysis. Sit-to-stand transitions have been studied with optical motion trackers [24] and force plates [25]. Although these methods provide very detailed and granular information about the movements, they are limited to the laboratory environment [7, 17].
The laboratory setting can only assess the performance of the participants in the confined environment (e.g. in-clinic) while individuals demonstrate different behavior in real-life daily activities [26, 27]. For example, sit-to-stand duration has been shown to be higher during daily activities compared to the functional test performed in the clinic in older adults and in patients with idiopathic Parkinson’s disease (IPS) [28]. Thus, it is important to develop methods that can also be used in domestic environments.
Inertial sensors can be applied in almost every environment. Moreover, they have been already used to instrument the 5xSTS [29] or the 30SCT [30] tests. Kinematic parameters extracted from such instrumented assessments have been shown to have greater clinical relevance than the conventional clinical approach [31, 32]. Wearable sensors have provided an objective tool to evaluate PTs during daily activities as well. Barometric pressure sensor within the pendant device was used as a complementary source of data to detect the PTs [3, 33]; however, due to the pressure changes in outdoor environments, the use of the barometric sensor can adversely affect the detection accuracy. For instance, in [3], the sensitivity of the sit-to-stand detection was decreased by 25% in outdoor environments.
There are some studies on monitoring sit-to-stand transitions with a single inertial sensor on either the sternum or on the lower back. In reference [34], the gyroscope and accelerometer signal along with a discrete wavelet transform have been used to obtain the trunk angle and consequently to detect the PTs. A simpler sensor setup with only a tri-axial accelerometer was used in [35]. In this study, the tilt angle of the trunk was estimated by the scalar product of the accelerometer data and gravity vector obtained during a static calibration at the beginning of each measurement. These studies were validated under very controlled conditions that involved sit-to-stand and stand-to-sit movements with a few other activities. More daily activities were included in the measurement protocol used by [36] and to reduce the false positive trunk movements, fuzzy rules have been employed to improve the accuracy of detection based on the previous or next activity. The performance of the PT detection was further improved by employing a template matching technique with dynamic time warping method in [37]. However, the performance of the detection algorithm was still unsatisfying with a positive predictive value and sensitivity of 22% and 50%, respectively. In another study, with a single inertial sensor on the waist, the candidates of the PTs were first detected by detecting the peaks of the tilt angle of the lower back. These were filtered out by double integrating the vertical acceleration and calculating the elevation change of the lower back [38].
The drawback of all of these studies [21, 34–40] is that they require the sensor to be attached to a specific and fixed location of the body, which is difficult to maintain during daily activities and may not be achievable by patients themselves without a trained operator, thus limiting its broaden applicability in clinical setting.
This issue has been partially solved through using the signal vector magnitude which is the Euclidean norm of the accelerometer signal [41, 42]. The choice of various wavelets and scale approximations were studied in [42] to detect the PTs in a large group of healthy younger and older adults. However, in both of these studies, no method was suggested to distinguish true PTs from movements that can have similar wavelets to PTs. Their algorithms have been validated in measurements involving only sit-to-stand and stand-to-sit movements with rest periods in-between.
To this end, an algorithm which is robust to sensor placement changes and validated in a range of daily activities is desirable. Furthermore, little is known about the transferability of algorithms developed within a certain cohort, to other cohorts (e.g., with different and without diseases). The goal of this study was therefore to evaluate the performance of a new PT detection algorithm in healthy individuals and patients with different diseases that were all equipped with an inertial sensor on different locations around the waist and on the trunk. The new algorithm was validated in both laboratory and daily activity settings. Finally, the effect of the sensors location on the detection performance and extracted parameters was evaluated.
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