A performance analysis of a wireless body-area network monitoring system for professional cycling

3.1 Wireless sensing system

The sensing system (see Fig. 1) is composed of several body area wireless inertial sensor nodes based on the ProMove platform [15]. ProMove creates a bridge between inertial measurement units (IMUs) and wireless sensor networks (WSNs) by embedding in one device the following:

• 8 degrees-of-freedom inertial sensors: ±1.5 to 6 g three-axial accelerometer, ±500°/s two-axial gyroscope and 6 gauss three-axial magnetometer.

• Low-power MSP430 micro-controller, widely used in “motes”-like sensor node platforms.

• IEEE 802.15.4-compliant system-on-a-chip (SoC) for low-power wireless networking.

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The micro-controller is dedicated for sampling the inertial sensors and running specific data processing algorithms. The SoC provides the developer with the necessary computational and memory resources for implementing the wireless networking protocol stack. This facilitates a two-tier approach and a beneficial separation of resources between sensor data processing and wireless networking.

3.2 Measurement of kinematic parameters

As shown in Fig. 2, we use three ProMove nodes placed along the left leg of the cyclist, on the thigh, shank and foot. For each node, we compute its orientation relative to the Earth reference frame in terms of roll, pitch and yaw (R-P-Y) angles. By combining the orientation of each node, we obtain the joint motions of the three segments of the lower limb. In our current experiments the kinematic parameters of interest are the knee and ankle joint angles, noted with K and A, respectively. As depicted in Fig. 3, both K and A can be computed from the roll angles derived from the motion of the three nodes in the sagittal plane, as follows:

$$ \begin{aligned} K&= \pi - R_1 - R_2\\ A&= R_2 + R_3 \end{aligned} $$

(1)

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The measurement process can be therefore divided in two steps: (1) the computation of the orientation of each node and (2) the determination of the joint angles.

3.2.1 Orientation computation

At any given time, the stationary condition is detected by analyzing the variance of the acceleration magnitude over a sliding time interval. If the variance is below a given threshold, then the node is considered stationary and its orientation is computed using the accelerometer and compass readings: considering that a _x , a _y and a _z are the projections of static acceleration on x, y and z axis (i.e. the readings of the accelerometer sensor), the roll and pitch angles are computed as follows [16] (see also Fig. 4a):

$$ \begin{aligned} R &= \hbox{arctan}\left(\frac{a_y}{\sqrt{a_x^2+a_z^2}}\right)\\ P &=\hbox{arctan}\left(\frac{a_x}{\sqrt{a_y^2+a_z^2}}\right) \end{aligned} $$

(2)

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Considering that c _x , c _y and c _z represent the projections of the magnetic field on the three axes (i.e. the readings of the compass sensor), then the yaw angle Y is computed as follows [17] (for brevity we use the short trigonometric notations cα, sα, tα to denote the cos, sin, tan of angle α):

$$ Y =\hbox{arctan}\left(\frac{c_x cR + c_z sR}{c_y cP + (c_x sR-c_z cR) sP}\right) $$

(3)

During cycling (i.e. when the variance of the acceleration magnitude exceeds the given threshold), the R-P-Y angles are updated using the gyroscope readings g _x , g _y and g _z . Given the fact that the nodes have only a 2D gyroscope, the angular velocity g _z is assumed 0. Using the rotation matrix [18]:

$$ M = \left(\begin{array}{ccc} 1 & tP sR & -tP cR \\ 0 & cR & sR \\ 0 & -sR/cP & cR/cP \end{array}\right) $$

(4)

we compute the new angles R′, P′ and Y′:

$$ \left( \begin{array}{c} R^{\prime} \\ P^{\prime} \\ Y^{\prime} \\ \end{array} \right) = \left( \begin{array}{c} R \\ P \\ Y \\\end{array}\right) + M \left( \begin{array}{c} g_x \\ g_y \\ 0 \\ \end{array}\right) $$

(5)

A well-know problem of Eq. 5 is the effect of error accumulation due to the integration of the gyroscope data. During stationary periods, the orientation angles can be reset to the values computed through Eqs. 2 and 3, but while moving the errors can accumulate in time indefinitely. Our approach to this problem is to exploit the repetitive pattern of the cycling motion and to reset the orientation angles to values computed from the compass readings. The advantage of the compass sensor is that it gives an absolute attitude measure, without suffering from error accumulation. The disadvantage is represented by possible disturbances of ferrous or magnetic nearby structures, but this problem is relatively minor in our case because most modern bikes have lightweight non-metallic structures. For a more detailed discussion on these aspects, see Sect. 6.

The reset is applied when the lower limb is at the pedal-up position, which corresponds to a minimum peak in the roll angle. If we denote with st the values obtained during the most recent stationary period (using Eq. 2) and with pk the values when reaching the pedal-up position, we compute the new roll angle (see also Fig. 4b):

$$ R_{pk} = R_{st} - C_{st} + C_{pk} $$

(6)

where \(C_{st}=\hbox{arctan}(\frac{c_{z,st}}{c_{y,st}})\) and \( C_{pk} =\hbox{arctan}(\frac{c_{z,pk}}{c_{y,pk}})\).

The pitch angle is assumed to remain unchanged, i.e. P _peak  = P _static and the new yaw angle Y _peak is derived using R _peak and P _peak in Eq. 3 (see Sect. 6 for a discussion of this assumption). Until the next pedal-up position, the integration step continues as in Eq. 5.

4.1 Objectives

The main objective of our experiments is to validate the performance of the measurement algorithm presented in Sect. 3.2, using the ProMove sensor nodes, with respect to the reference camera system. In addition, we would like to investigate possible optimizations of the data processing and to evaluate the quality of the wireless communication.

4.2 Sensor sampling and communication

Sampling at high rates (≥100 Hz) the inertial sensors and communicating all the raw data is difficult due to the limited hardware available on sensor nodes (see recent work of Bosch et al. [19] for a detailed discussion of practical problems). The ProMove nodes alleviate the problems to a large extent, by providing the full power of the MSP430 micro-controller for handling the sensors, while the wireless communication protocols can run separately on the SoC.

During initial tests we found the IEEE 802.15.4 implementation to be inappropriate for gathering data from all three nodes at the desired rates. Therefore, in our experiments we use a specialized MAC protocol developed for ProMove nodes, FastMAC, with which we can collect data from all the inertial sensors at rates of up to 250 Hz, with synchronization among nodes in the range of microseconds. It is important to mention that FastMAC could be also used to implement the communication strategy described at the end of Sect. 3.2.2, possibly at a much lower duty cycle. However, a good synchronization among the nodes is essential in all cases, as the computation of the joint angles has to use orientation information derived from all nodes at the same time instance.

4.3 Experimental setting

Nine subjects participated in our experiments (6 male and 3 female), one being a semi-professional cyclist (see Table 1). All subjects performed the test with their own bikes positioned on roller cylinders. For non-professional cyclists, the bike was secured and a support was provided to them to ensure their total safety (see Fig. 5a). For the cyclist, both wheels revolved on the cylinder roller and he balanced his bicycle just as if he was riding normally (see Fig. 5b). Each experiment consisted of five sessions corresponding to five pedalling cadences: 40, 60, 80, 100 and 120 rpm. In each session the subject was asked to cycle for one minute at a target cadence. As a final experiment, the semi-professional cyclist performed a ride of approximately five minutes at free cadence, in order to verify the robustness of our solution to sensor drift and accumulation of errors.

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Table 1

Participants details

No.	Gender	Age	Height (cm)	Body mass (kg)	Cycling
1	M	30	172	80	Commuting
2	M	31	180	73	Recreational
3	F	24	164	54	Commuting
4	F	22	166	64	Recreational
5	M	30	172	62	Commuting
6	M	23	182	68	Competition
7	F	30	172	50	Commuting
8	M	25	181	76	Commuting
9	M	38	185	90	Commuting

We collected data from both the sensor nodes and the reference motion analysis system (Optotrak Certus, Northern Digital Inc. [7]). The Optotrak system was used as a gold standard laboratory approach. Infra-red markers placed at the anterior surface of the mid-thigh, the anterior surface of the mid-shank and the superior surface of the mid foot on the left lower limb were tracked while subjects were pedalling. A non-collinear marker cluster technical frame composed of four tracking markers was created with the unit sensors serving as a rigid structure to define an arbitrary three-dimensional orthogonal segment coordinate system [20]. Using the foot, shank and thigh tracking markers and their respective distal and proximal anatomical landmarks, Visual3D model building software [21] was used to reconstruct anatomically meaningful segment coordinate system.

Data was sampled at 200 Hz for both the sensor nodes and the camera system. Before each experiment (i.e. once for each subject), both the sensor nodes and the camera system were calibrated. For the sensor nodes, we calibrated the static acceleration using the method described in [22] and the magnetic compass using the method presented in [23]. We chose these methods for their simplicity (complete calibration of the three nodes takes about 5 min), as it is not realistic to assume that users would be willing to go through intricate calibration procedures.

The raw data obtained from sensor nodes was processed on the computer using Matlab, in order to derive the joint angles defined in Sect. 3.2. The accelerometer and compass are used to establish the orientation of each node (R-P-Y angles) in static mode, at the beginning of each experimental session. During cycling, the gyroscope and compass are used to measure the variation of the roll angle and subsequently compute the joint angles. The processing algorithm is kept lightweight so that it is feasible to implement on sensor nodes. For determining the measurement accuracy of the joint angles using sensor nodes, we use as reference the values provided by the Visual3D software.

A sample video of one experiment showing data collection from the wireless sensor nodes is available at [24].

5.1 A typical experimental session

Figure 6 shows a portion of around 7 s from an experiment performed at 100 rpm. For all the plots, the horizontal axis represents the time in seconds and the vertical axes depicts the angle in degrees computed both by our algorithm using the wireless sensor nodes (continuous line) and the reference Optotrak system (dotted line).

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The first three plots compare the absolute thigh, shank and foot angles, as measured by the two systems. We notice the clear repetitive pattern, with ranges of motion of approximately 40°. We also observe that the actual cadence achieved by the subject is about 94 rpm. The bottom plots show the joint knee and ankle angles, computed as explained in Eq. 1. In particular, plots 5 and 7 show the peaks of the joint angles. We notice that the results of our algorithm follow closely the Optotrak reference, with small differences in the signal amplitude, which are reflected in the differences between peaks. Even if the signal has small disturbances, for example between seconds 5 and 6 for the ankle angle, our system manages to reproduce it quite accurately. Interesting to notice is the reset procedure, which occurs for example just before second 8 in the foot angle, and which is reflected in a sudden shift of the signal in the ankle angle.

5.2 Accuracy results

Figure 7 shows the RMSE values computed for each experiment (cadence and subject). Figure 7a shows the errors at the peak values, while Fig. 7b summarizes the errors for all the values of the joint angles. The results are grouped horizontally based on the cadence. Each plot shows in the left half the knee angle RMSE values and in the right half the ankle angle RMSE values, for all the nine subjects that participated in the experiments (subject number 6 is a semi-professional cyclist).

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Figure 8 gives a snapshot of the results obtained in our last experiment, where the semi-professional cyclist performed a ride of approximately five minutes at free cadence. The purpose of this experiment was to verify the robustness of our solution to known issues of inertial sensors, such as drift and accumulation of errors. The plots show the peak values of the knee and ankle angles during approximately one minute at the beginning of the experiment (left) and at the end of experiment (right). The results confirm that there is no significant drift or accumulation of errors in time, due to the reset procedure applied every cycle. The RMSE values for the overall experiment remain in-line with the results obtained for the one-minute experiments, for example 2.43° for knee angle peak values and 3.38° for ankle angle peak values.

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5.3 Wireless communication

Along with the sensor data, we collect information related to the received signal strength indication (RSSI) and sequence numbers of each radio packet. The latter allows us to identify lost packets and fill in the gaps in the data using linear interpolation.

Figure 9a gives an example of how the RSSI varies during a typical experiment. The information is collected from the node on the thigh. We observe the clear repetitive pattern following closely the pedal revolution motion. The frequency of the low peaks corresponds to approximately 57 rpm and proves to be a reliable indicator of the pedaling cadence (the metronome was set to 60 rpm in this experiment). Figure 9b summarizes the RSSI values for all subjects, for each node and cadence. The overall mean RSSI values are −53, −50 and −48 dBm for the thigh, shank and foot node, respectively. The nodes on the thigh and foot exhibit a minor linear degradation (maximum 2 dBm) of the RSSI with the cadence. However, this has no significant impact on the packet loss, which remains relatively stable for each node throughout the experiments, as shown in Fig. 9c. The overall mean packet loss rates are 6.9, 8.9 and 7.9% for the thigh, shank and foot node, respectively.

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Based on these results we can conclude that the pedaling cadence does not significantly impact on the packet lass rate and that the FastMAC protocol is robust enough to handle sudden drops in the RSSI values. The packet losses occur due to motion, as in the static situation we register loss rates of less than 0.1%. A more detailed analysis of the packet losses indicates that they occur in bursts, with 4 consecutive packets lost on average.

6.1 Advantages

The major progress beyond state-of-the-art is that such a miniaturized sensing system can assist cyclists outdoors, during training or racing. No wires, simple strap-on and instant feedback represent thus direct key benefits for the users. Additionally, the human movement and sports scientists are provided with extensive insight into the body kinematics in real-life conditions.

6.2 Limitations

Portable sensors are required to be as small as possible, which translates into a number of resource limitations related to: battery capacity, computational power, sensing accuracy, robustness of wireless communication. All these factors impact negatively on the system accuracy. In addition, compared to camera-based systems, inertial measurement solutions suffer from the inherent problem of error accumulation via integration and possibly from the approximations introduced in the reset step.

6.3 Assumptions in the reset step

In Sect. 3.2, we made the assumption that the pitch angle does not change at peaks. To backup this assumption, we compute the absolute difference between the minimum and the maximum pitch angles that the Optotrak system measures at peaks during all experiments. This absolute difference is 4° on average, which has negligible influence in the rotation matrix in Eq. 4.

6.4 Accuracy improvements

6.5 Filtering

6.6 Energy consumption

The lifetime of the battery-powered sensor nodes is a constant concern. ProMove consumes approximately 40 mA in full-sampling (8 channels at 250 Hz) and communication mode. On a battery of 1,500 mAh, this translates to more than 36 h of uninterrupted functioning. If the system is used 8 h per day, batteries should be charged once in 4 days.

6.7 Lessons learned

Firstly, our experiments showed the net advantage in usability when using wireless sensor nodes compared to the camera system. The complex set-up procedures, installation of wires and markers, as well as the confinement to a limited space in the lab, made the experiments with the camera system extremely laborious. In contrast, the sensor nodes required only a few minutes for calibration and mounting, after which the data could be immediately collected and processed. Secondly, the performance evaluation shows that lightweight algorithms, which are simple enough to run on low-power micro-controllers, can achieve a satisfactory degree of accuracy. Complex filtering might not always be needed, depending on the application specifics. Thirdly, it would be of much interest to determine the actual performance of our method in real-life, outdoors cycling. However, the main difficulty in achieving this is the impossibility of using camera systems in real-life conditions, in other words the lack of a reliable reference system. Finally, a point of attention in our solution remains the effect of magnetic disturbances. The lab in which the experiments were conducted exhibited such disturbances, which were partly compensated through calibration. We can expect accordingly a possible increase of accuracy in outdoors conditions.

6.8 Usability

We interviewed professional cyclists, cycling coaches and sport scientists from Australia and The Netherlands to find out if such a system would be of interest to professional riders and to get a view on the look-and-feel and the type of feedback the system should provide. They all agreed with the potential of the system for performance enhancement and overuse injuries prevention. The system offers an appealing solution to analyse the evolution of the lower limb kinematics over time, especially for an early detection of changes occurring at the onset of fatigue.

The system could be used to provide visual feedback allowing fatigue-induced alterations of the pedalling technique to be detected so that cyclists can continuously adjust their pedalling technique to maintain a high level of performance and limit the risk of injuries during training sessions and races. Future developments of the sensing system will be undertaken to reduce the size of the sensors and integrate them into the cyclists’s clothing and/or bikes. Simple and instantaneous visual feedback about pedalling technique could be displayed through the miniaturized bike computers fixed to the handlebars of the bikes used by cyclists. An important suggestion recurring in most interviews was to integrate the information related to kinematic parameters with power measurements from existing commercial systems (e.g. SRM). The ideal system would be composed of several dedicated wireless sensors, such as the ProMove nodes, but smaller and integrated into clothing and bike, and one bike computer or mobile device that can fuse kinematic information with power measurements in order to provide feedback to the cyclist.