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Author: Che-Chang Yang (2006-09-01); recommended: Yeh-Liang Hsu (2006-09-01).
Note: This article is Chapter 5 of Che-Chang Yang’s Master thesis “Development of a Portable System for Physical Activity Assessment in a Home Environment.”

Chapter 5. Algorithm design for posture and movement identification

This chapter describes the development of the algorithm embedded in the microcontroller of the DAU for posture and movement identification. It is designed to identify three still postures and five posture transitions from the signal output of the accelerometer attached to the waist. The test results of the algorithm are also presented.

5.1     Acquisition of the trunk tilt angle and acceleration components

Human body movement produces complex variations of accelerations and trunk inclination. Both piezo-electric and capacitance accelerometers respond to constant acceleration such as the force of gravity. This can be used for calibration of the accelerometers and measurement of the tilt angle. High-pass filters with low cut-off frequencies are often used to eliminate the interference of DC response. [Bouten et al., 1997, Mathie, 2003, 2004]. This study also uses a DC-responsive accelerometer. However, instead of using high-pass filters to eliminate DC response, mathematical formulas are used in this study to calculate tilt angle and accelerations. It is expected to obtain accurate results regardless of existence of DC response.

Figure 5.1 illustrates the orientation of the accelerometer moving with human body at a given instant. The Z and Y axes of the accelerometer initially coincide with the antero-posterior (horizontal) direction and the vertical direction, respectively. At this instant, acceleration produced by the movement can be decomposed into a horizontal component  and a vertical component . The movement also causes a trunk tilt angle q. The Z axis voltage output () and Y axis voltage output () can be expressed as Equation (5.1) and Equation (5.2), respectively. Note that in the equations, 1.65V is the voltage output at zero gravity, and 0.66 is the coefficient for per 1g acceleration. The trunk tilt angle q represents tilt angle relative to the vertical or horizontal line. Due to the response to constant gravitational force (1g), the actual vertical component acquired by the accelerometer is . The trunk tilt angle q can be obtained by Equation (5.3) and Equation (5.4).

Figure 5.1 Acceleration with inclination





With the trunk tilt angle q obtained from Equation (5.4), the vertical acceleration  can be obtained with Equation (5.5) or Equation (5.6) derived from Equation (5.1) and Equation (5.2).




Finally, the antero-posterior acceleration component  can be obtained by the simple trigonometric relation:


Figure 5.2 shows an example of the signal outputs of the Y and Z axes during normal sit-to-stand posture transition. Figure 5.3 shows its trunk tilt variation, and Figure 5.4 shows the acceleration components  and  converted from the signlas.

Figure 5.2 Signal outputs of the accelerometer during sit-to-stand posture transition

Figure 5.3 Trunk tilt angle variation during sit-to-stand posture transition

Figure 5.4 Acceleration variations in vertical and antero-posterior directions during sit-to-stand posture transition

5.2     Algorithm design

This section describes the algorithm designed to identify three postures (lying still, sitting still, and standing still), four postural transitions (sit-stand, stand-to-sit, sit-to-lie and lie-to-sit) and walking movement. Detection for possible falls is also designed in this algorithm.

5.2.1 Structure of the Algorithm

Figure 5.5 describes the process flow of the algorithm. In this figure, each block represents a separate program section for signal processing or identifying one possible posture or activity. As shown in Figure 5.5, the algorithm includes five main parts: data sampling (Cx), pre-processing (Px), dynamic posture transition identification (DBx), still posture identification (DAx) and possible fall detection (DCx).

The data sampling process consists of the primary stage (C1) and secondary stage (C2). Initially, the process D1 detects whether any sign of dynamic movement exists from the 0.5s data collected in the primary stage. If no dynamic movement is detected (D1=No), the 0.5s data is used to identify one of the possible still postures in the processes DAx. If dynamic movement is detected (D1=Yes), the secondary data collection stage (C2) is immediately activated to collect the subsequent 2.0s data. The 2.5s data collected in both stages is then combined to represent an “event” for the following step-by-step identification processes. This dual-stage data sampling strategy ensures that the data of one event can be acquired within the same sampling interval.

In the pre-process, three data sets in series of an event, q,  and  are inputted into the algorithm. Median filtering is first employed to the data series in section P1. Section P2 simplifies the data series, then Section D2 determines whether the event is a dynamic movement. If the trunk tilt changes apparently throughout the data spectrum, the event will be regarded as a possible posture transition (D2=Yes). Four posture transitions (two sit-stand transitions and two lie-sit-transitions) and walking are identified in the following sections (Sections DBx). If the event is recognized as a still posture (D2=No), Sections DAx will be used to identify one of three possible postures. As shown in Figure 5.5, Sections DCx are used for fall detection. All the identified results are stored in the variable STATE. In the case where there is no definite result determined throughout the processes, the event will be recorded as an “Uncertain movement” or an “Uncertain posture”.

Figure 5.5 Flowchart of the algorithm

Computation capability and program capacity of the PIC microcontroller used in this study are relatively limited compared to powerful mathematical computation software on PCs. Therefore it is crucial to save memory and minimize time delay within the computation process when designing the algorithm. In addition, all signals are processed in time domain analysis in the algorithm because batch data analysis in frequency domain method yields inaccurate results. In this study, the tri-axial signals are analyzed to identify one of the eight events every 0.5 or 2.5 seconds. The sampling rate for A/D conversion is set 60Hz due to the conclusion that most human movements are below 20Hz [Bouten et al, 1997].

5.2.2 Pre-processing and action determination

As shown in Figure 5.5, pre-processing is applied for the 2.5s combined data after the secondary stage (C2). Preprocessing includes median filtering (P1) and data simplification (P2). Median filtering is a common technique to remove high frequency spikes in the data. Data simplification is then conducted for all the data series of the accelerometer outputs. The “slope mapping” technique is used in action determination (D1, D2) following preprocessing. Note that median filtering and data simplification processes are not employed to the data collected in the primary stage to save computation time.

(1)   Median filtering (P1)

In the beginning, 152 combined data points (VZ, VY) are collected in a 2.5s time interval (at approximately 60Hz). The data series for trunk tilt, vertical acceleration, and antero-posterior acceleration are obtained by Equations (5.1) to (5.7). Then median filtering is applied to the two acceleration components. An example of the effect of median filtering is shown in the Figure 5.6. This comparison reveals that median filtering has the ability to remove excessive spikes which are far removed from the overall tendency of the data. As a result, n-2 point data series can be obtained from the original n-point data series after performing median filtering with window length of 3. In other words, this technique generates a 150-point data series from the original 152-point one. From empirical observation, the trunk tilt data series has less fluctuation and spikes than the other two data series. Therefore, the median filtering is not applied to the trunk tilt data.

MATLAB Handle Graphics

Figure 5.6 An example of the effect of median filtering

(2)   Data simplification (P2)

In order to reduce the computational load of the PIC microcontroller, the data series are further simplified prior to data analysis. Three neighboring points (p3i-2, p3i-1 and p3i) in a data series are combined to yield one average data point, , as shown in Equation (5.8).


After the pre-processing, a data series with 3n data points are reduced to a smaller data series with n averaged points. Equation (5.8) is used to simplify the data series of trunk tilt and the two acceleration components. This also lowers or eliminates subtle fluctuations generated by external vibration, power instability, or even the slight effect of the discharging capacitors on the accelerometer. Figure 5.7 shows an example of trunk tilt data series during the sit-to-stand transition. Figure 5.7(a) shows the original data series converted from raw signal output, and Figure 5.7(b) shows the same data after the pre-processing by Equation (5.8). This simplification process provides preferable effects in reducing the amount of data points and undesirable fluctuations, while preserving most apparent characteristics of the original data series.

MATLAB Handle Graphics

Figure 5.7 Simplification for trunk tilt data series

(3)   Action determination (D1, D2)

Sections for action determination (D1, D2) determine whether the event is a dynamic movement by examining the variation of the trunk tilt angle. Section D1 processes the 0.5s data collected in the primary stage (C1), and Section D2 processes the 2.5s combined data.

The “slope mapping” technique is used to detect whether there is apparent fluctuation in the trunk tilt data series. Consider a simplified trunk tilt data series  for i=1 to n. The slope  between two neighboring data points  and  is calculated and a segment slope series  with n-1 data points is obtained from the original n-point data series.

The segment slope series S is then mapped to another data series C, which is used to record whether there is apparent fluctuation in the simplified trunk tilt data series by checking the slopes in the segment slope series S. As shown in Equation (5.9), if the absolute value of the slope si is equal to or greater than a predefined threshold, then  is registered 1 in the data series. On the contrary,  is registered 0 if si does not reach that threshold. Figure 5.8 shows the result of the slope mapping for the trunk tilt data series of a sit-to-stand transition. Finally, if the amount of  registered 1 is greater than a predefined number, this event is classified as a dynamic movement. Section D1 and D2 use the same method but different parameters. After a few trials, the parameters used in D1 are , , while the parameters used in D2 are , .


MATLAB Handle Graphics

Figure 5.8 Slope mapping for trunk tilt data

5.2.3 Identification of sit-stand transitions

Referring to Figure 5.5, if an event is identified as a dynamic movement in Section D2 (D2=Yes), Section D3 decides whether the upper body of the tester is in an upright position by checking if the trunk tilt angle is within 30 degrees forward or backward from the vertical plane. An “upright” state is recognized if more than 35 data points out of the total 50 data points in the simplified trunk tilt data series are in this tilt range.

If an event is recognized as “dynamic” and “upright” in Section D2 and D3, it might be classified as either one of the sit-stand transitions or a walking action. Sections DB1 and DB2 determine whether the event is a “sit-to-stand” or “stand-to-sit” transition.

In order to investigate the accelemetric characteristics of sit-stand transitions, a test was performed on 15 ostensibly healthy subjects in various ages. The recruited subjects were healthy in mobility except two elderly subjects who required the aids of the armrests or sticks for both posture transitions. The subjects were arranged in three groups according to their ages: young (20-35yrs), middle-aged (35-50yrs) and elderly (50+yrs), with 5 subjects in each group. The subjects were asked to perform slow, normal and fast sit-stand transitions as close as possible to how they perform these actions habitually in their activities of daily livings. Each subject performed each transition in each transition pace at least three times, respectively. The elder subjects only performed the transitions in normal pace. There are a total of 98 sit-to-stand data samples and 101 stand-to-sit data samples collected in the test.

Figure 5.9 and Figure 5.10 show examples of normal sit-stand transition acceleration patterns acquired from a young subject in the test. As shown in Figure 5.10, vertical acceleration components are more sensitive than the antero-posterior components in both sit-stand transitions. Therefore, only the vertical components are used to identify either sit-to-stand or stand-to-sit posture transition.

Figure 5.9 Example of acceleration patterns of a normal sit-to-stand transition

Figure 5.10 Example of acceleration patterns of a normal stand-to-sit transition

Figure 5.11 shows an example of the vertical acceleration components of the sit-to-stand and stand-to-sit transitions. For the sit-to-stand case in this figure, it is observed that the positive acceleration peak occurs prior to its negative peak and vice versa for the stand-to-sit case. This pattern exists in all samples collected in the test. Therefore the first rule in Sections DB1 and DB2 is to compare the order of occurrences of those two peaks. If the positive peak occurs prior to the negative peak, this event could be a sit-to-stand transition. On the contrary, if the occurrence of the positive peak falls behind the negative peak, the event could be a stand-to-sit transition.

Figure 5.11 Example of common acceleration patterns of sit--stand transitions

Furthermore, the time interval (or peak distance) between the positive peak and the negative peak  is considered. Figure 5.12 and Figure 5.13 show an example of vertical acceleration components during fast, normal, and slow sit-stand transitions from the same young subject. Faster transitions produce shorter peak distance, and vice versa. For example, the patterns of sit-to-stand transitions shown in Figure 5.12 have the peak distances in 0.36s, 0.54s and 0.96s for fast, normal, and slow transitions respectively. In the Figure 5.13, they are 0.16s, 0.72s and 1.82s for fast, normal and slow stand-to-sit transitions, respectively. Table 5.1 shows the averaged peak distances of sit-stand transitions from all the subjects’ data. These values also reveal the evident tendency mentioned above.

Figure 5.12 Comparison of vertical acceleration components during sit-to-stand transitions.

Figure 5.13 Comparison of vertical acceleration components during stand-to-sit transitions.

Table 5.1 Averaged peak distances of sit-stand transitions in different subject groups































Therefore, the second rule in sections DB1 and DB2 is to compare the peak distance. To find suitable parameters, the distribution of peak distances in sit-stand transitions of all samples collected in the test is shown in Figure 5.14. For the sit-to-stand case, most peak distances (95 out of 98) distribute over the range between 0.3s to 1.3s. For the stand-to-sit case, most peak distances (95 out of 101) distribute over the range between 0.4s to 1.5s. As a result, the upper and lower bound of the peak distances, and , can be assigned according to the results in Figure 5.14. In this algorithm, the parameters for sit-to-stand transition are s and s. For stand-to sit case, they are s and s.

Figure 5.14 Distribution of peak distance in sit-stand transitions among all the subjects

The third rule in Sections DB1 and DB2 examines the peak acceleration values. From Figure 5.12 and Figure 5.13, fast transitions tend to produce high peak values, and vice versa. Table 5.2 also shows the averaged peak values of sit-stand transitions in different subject groups, and Figure 5.15 and Figure 5.16 further show the distributions of the peak values from all samples collected in the test. According to the data, for the sit-to-stand case, most positive peak values (88.7%) are greater than 0.1g, and all negative peak values are less than 0g. For the stand-to-sit case, most positive peak values (76%) are greater than 0.1g, and most negative peak values (93%) are less than -0.1g. Note that from the empirical results, the positive acceleration peaks of stand-to-sit transitions are usually higher. It is primarily due to the effect of the reacting force exerted by the support (chairs, bed, etc.) when sitting.

Therefore, the threshold parameters  and  are given for the positive and negative peak values, respectively. For the sit-to-stand case, the thresholds g and g are assigned. For the stand-to-sit case, the thresholds g and g are assigned. If a dynamic event with its positive acceleration peak greater than , as well as its negative acceleration peak less than , it could be a sit-to-stand transitions.

Table 5.2 Averaged peak values of sit-stand transitions in different subject groups







Max. av (g)




Min. av (g)




Max. av (g)




Min. av (g)





Max. av (g)




Min. av (g)





Max. av (g)




Min. av (g)




Max. av (g)




Min. av (g)





Max. av (g)




Min. av (g)




Figure 5.15 Distribution of positive vertical acceleration peaks of sit-stand transitions in different subject groups

Figure 5.16 Distribution of negative vertical acceleration peaks of sit-stand transitions in different subject groups

One particular acceleration pattern shown in Figure 5.17 may occur during faster stand-to-sit transitions. Overshooting accompanying with a high reaction force may result in a negative acceleration peak whose position follows the positive peak closely. If this situation occurs, the algorithm may conclude that it is a sit-to-stand transition (according to the first rule discussed above). Such a paradox is solved by searching for a local negative peak which locates prior to the positive peak when applying the third rule discussed above. The updated data is then re-inputted to the three rules for identification. Therefore, the stand-to-sit transition with such a special acceleration pattern can be identified as well.

The “stand-to-sit” or “sit-to-stand transition” can be identified only when all three rules discussed above are satisfied. Otherwise Sections DB1 and DB2 fail to identify the dynamic event and result in an “Uncertain movement”.

Figure 5.17 Particular acceleration pattern of fast stand-to-sit transition.

5.2.4 Identification of walking movement

Figure 5.18 shows an example of body acceleration patterns when walking at normal gait. The tendency that positive peaks in the vertical component appear in accordance with negative peaks in the antero-posterior component can be observed in this figure. In addition, the magnitude of positive acceleration peaks, which are caused by the reacting forces exerted by the ground, is higher than that of the negative ones. Again it is also observed that the vertical acceleration component is more sensitive to the walking movement than the antero-posterior component. Therefore, the vertical acceleration component is used to identify walking movement.

Mathie’s [2004] study on the interpretation of human movement patterns reported that the frequencies of walking movement range from 0.7Hz to 3.0Hz. Frequencies in 1Hz to 4Hz have also been reported in a continuous walking record [Sekine et al, 2000]. Higher walking frequencies also result in higher positive acceleration peaks in vertical component.

Figure 5.18 Acceleration pattern when walking

In Section DB5, walking movement is identified by investigating the apparent changes (slopes) in vertical acceleration component. The slope mapping technique discussed in Section 5.2.2 is applied again here. Figure 5.19 simplifies the vertical acceleration pattern into the triangular forms in order to define the parameters in the algorithm. In figure 5.19, slow walking at 1Hz is used as frequency f. Referring to the previous studies, the values of positive and negative acceleration peaks in the vertical component are approximately 0.4g and -0.3g, respectively. From these values, the slope threshold for walking movement is 0.07g per sampled point. However, the threshold was tuned to 0.15 g per sampled point to avoid being too sensitive to the signal fluctuations or external vibrations.

Finally, a dynamic event is recognized as “walking” if the number of the segment slopes whose absolute values are greater than the slope threshold  reaches a given parameter NW=10 (out of the total 49 slope values representing an event).

Figure 5.19 Simplified vertical acceleration pattern of walking for the identification in this algorithm

5.2.5 Identification of lie-sit transitions

Lie-sit transitions, as illustrated in Figure 5.20, are characterized by rotational movements of the trunk which result in increasing or decreasing trunk tilt angles. Therefore, lie-sit transitions can be easily identified by investigating the trunk tilt variation.

Figure 5.20 Illustration of lie-sit transitions

Two rules are used in Section DB3 and DB4 in the algorithm to identify lie-sit transitions. The first rule is to calculate apparent changes in the trunk tilt angle using the slope mapping technique. Assume that  is the segment slope series of trunk tilt angle of an event to be identified. As shown in Equation (5.10), in series, ci is registered 1 if the segment slope si is equal or greater than a positive threshold value ( in this case, i.e., the trunk tilt angle increases by 3 degrees); ci is registered 2 if si is equal or less than a negative threshold ( in this case, i.e., the trunk tilt angle decreases by 3 degrees). If the number of ci registered 1 reaches or exceeds 10, this posture transition can be a lie-to-sit transition. Similarly, a dynamic event can be a sit-to-lie transition if the number of ci registered 2 reaches or exceeds the threshold value 10.


The second rule is to investigate the posture orientation at the end of the event. The posture orientation is flat at the end of a sit-to-lie transition, and is upright at the end of a lie-to-sit transition. In this rule, the last 10 data points out of the trunk tilt data are used to determine the posture orientation. Out of the 10 data points, if the numbers of data points with tilt angles between -60 degrees to -105 degrees is larger than 5, the posture orientation is concluded to be flat. On the other hand, if the numbers of data points with tilt angles between -30 degrees to +30 degrees, the posture orientation is concluded to be upright.

In Sections DB4 and DB5, lie-sit transitions are identified only when both rules are satisfied.

5.2.6 Still posture identification

Referring to the flowchart in Figure 5.5, if the event is not recognized as a dynamic movement in section D1 (D1=No) or D2 (D2=No), this event can be one of the three possible still postures: “lying still”, “sitting still” and “standing still”.

(1)   Lying still (DA1):

Section DA1 determines whether the still event is a lying posture. Karantonis et al. [2006] used the posture orientation (i.e., the tilt angle with respect to vertical line) to classify possible posture states including upright, lying and inverted. In Section DA1, the trunk tilt data is also used to identify the lying posture. Among the 50 data points that represent an event to be identified, if the number of data points with trunk tilt angle exceeds 60 degrees backward from the vertical plane is greater than 35, this event is identified as a “lying still”.

(2)   Sitting still and standing still (PA2, PA3)

If a still event is not identified as “lying still”, then it can be “sitting still” or “standing still”. However, both postures have upright trunk tilt angles. Therefore, distinguishing postures of sitting still and standing still requires the information of previous posture transitions or movement.

If a still event is not lying (PA1=No) and the previous posture transition is “sit-to-stand” or “walking”, this event is identified as “standing still”. On the other hand, the event is identified as “sitting still” if the previous posture transition is “stand-to-sit” or “lie-to-sit”.

5.2.7 Fall detection

For the elderly users, detection of accidental falls is an important issue. Intuitively, a fall can be regarded as a movement accompanying by unusual high acceleration peaks in a very short time interval. Several studies have suggested similar measures to characterize the event of a fall. Karantonis et al. [2006] used the signal magnitude vector (SVM) to evaluate the degree of the movement intensity. In fact, it was modified from the IMA (summation of the time integrals of the accelerometer outputs) proposed by Bouten et al. [1997] for evaluating energy expenditure due to body movements. This method has also been adopted in related studies to distinguish states of activity and rest. According to the definition given by Karantonis et al., falls are said to have occurred if at least two consecutive peaks in the SVM above a defined threshold are recorded. Its threshold of 1.8g was justified through formal patient studies. In their design scenario of real-time identification, if no activity occurs during this 60s post-fall interval after detecting a possible fall, the event is upgraded to a fall. Care-givers will be informed to take further measures and actions for this probably emergent situation.

Referring to the flowchart in Figure 5.5, if a dynamic event is not recognized as “upright” in Section D3, Sections DCx are then used to identify whether a possible fall occurs. A sign of possible fall is concluded if there are at lest two peaks at relatively higher magnitudes in either vertical or antero-posterior acceleration component. The threshold value used in Section DC1 and DC2 is . Therefore, a dynamic event is recognized as “sign of fall” if peak acceleration greater than 1.0g is detected.

When an event is identified “sign of fall”, the next step is to investigate whether the next event is a “lying-still” posture. If a “lying-still” posture is identified right after “sign of fall”, Section DC2 is activated (DC2=Yes) to count the number of “lying still” events in the following post-fall period. A “possible fall” event is identified in Section DC3 if the number of “lying still” events identified by the route of primary data collection in the flow chart in Figure 5.5 (C1èD1èDA1) reaches 30, or by the secondary data collection stage (C1èD1èC2èP1èP2èD2èDA1) reaches 5 in the following 20s period.

5.3     Performance evaluation

In order to evaluate the performance of the algorithm in still posture and dynamic activity identification, 10 subjects were recruited for the laboratory-based test. Sensitivity and specificity tests for posture (lying still) and posture transitions (sit-stand transitions, lie-sit transitions) and walking movement were conducted. Note that the evaluation did not include the sitting still or standing still postures due to the fact that both still postures are associated with the results of previously identified transitions or movement. Falling was not included either because it was not easy for the testers to simulate “standardized” falls.

Sensitivity is related to the degree of “type I error (false negative)”, which is defined as the error of rejecting something that should have been accepted. In the definition of sensitivity in Equation (5.11), “false negative” events refer to actual matches that were not detected, and “true positive” events mean actual matches that have been detected.


To evaluate the sensitivity of the algorithm, the subjects were asked to perform separate pairs of reciprocal transitions (sit-stand transitions and sit-lie transitions) or walking movements repeatedly in the identical fashion as they regularly behave in their daily living. In addition, they were also allowed to vary their movement paces or time intervals arbitrarily. The subjects performed each item 20 times according to the operator’s instructions, and a total of 200 samples for each test item were obtained. For the item of lying still, the subjects were in lying still on a bed for 50 seconds which corresponds to producing 20 identified events from the algorithm. Therefore, according to Equation (5.11), the sensitivity for each item is obtained with the numbers of the events which are correctly identified (true positives) divided by the total number of test samples 200.

Specificity is associated with the “type II error (false positives)”, which is the error of accepting something that should have been rejected. In the definition of specificity in Equation (5.12), “true negatives” refer to non-matches that have been rejected, and “false negatives” refer to the non-matches that have been accepted.


To evaluate specificity of this algorithm, the five test items were arranged in three courses: Course A (sit-to-stand and sit-to-lie), Course B (stand-to-sit and walk) and Course C (lie-to-sit). According to the algorithm, when a subject is sitting still, his possible posture transition from that state is either the sit-to-stand transition or the sit-to-lie transition. Therefore in Course A, the subjects were initially in the sitting still posture and then performed any movements other than the sit-to-stand or sit-to-lie transitions. For the same reason in Course B, the subjects were initially standing still, and then performed any movements other than stand-to-sit transition or walking movement. Similarly for Course C, the subjects performed any movements other than lie-to-sit transition as they were initially lying. The subjects performed 50 movements for each course in the test (for a total of 500 samples for each test course). Therefore, specificity is obtained with the number of rejected events divided by the total number 500 of performed events.

Note that the item of lying still is not included in specificity test. Also during the specificity test, the subjects were asked to avoid performing ambiguous movements. For example, in Course A, the subjects were asked to avoid performing a movement similar to the sit-to-lie transition even though they subjectively considered it a free movement.

Table 5.3 shows the evaluation results of sensitivity and specificity from 200 and 500 samples, respectively. The sensitivity and specificity of the algorithm for all posture identification are at acceptable levels.

Table 5.3 Performance (sensitivity and specificity) of the algorithm

Posture/activity item

Sensitivity (%)

Specificity (%)

Lying still



















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Karantonis, Dean M., Narayanan, Michael R., Mathie, Merryn., Lovell, Nigel H., Celler, Branko G., 2006. “Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring,” IEEE Trans. Info. Tech Biomed., vol. 10, No. 1, pp. 156-167.

Mathie, M. J., Coster, A. C. F., Lovell, N. H. Celler, B. G., 2003. “Detection of daily physical activities using a triaxial accelerometer,” Medical & Biological Engineering & Computing, vol. 41, pp. 296-301.

Mathie, M. J., Celler, B. G., Coster, A. C. F., Lovell, N. H., 2004. “Classification of basic daily movements using a triaxial accelerometer,” Medical & Biological Engineering & Computing, vol. 42, pp. 679-687.

Sekine, M., Tamura T., Togawa T., Fukui Y., 2000. “Classification of waist-acceleration signals in a continuous walking record,” Medical Eng. and Phy. Vol. 22, pp. 285-291.