LA COMPETENCIA DIGITAL. CONCEPCIÓN Y MARCO COMÚN DE REFERENCIA

Figure 6.5: Classification error rate on multiple subject with different pruning levels

only 25% of the time needed if no pruning algorithm is applied. Another aspect that must be considered is that during the transition period between two activities, some miss-classification may result. Although, according to [N⁺03] there are around 100 activity transition in a 12h period, if we chose a 100 as sliding window size we may have miss-classification for 333s in a 43200s period (0.78%).

6.8 Experimental result

To evaluate the effectiveness of the proposed method our PM has been tested on a sensor node and power consumption has been measured. The reference platform is constituted by the following components:

• Microcontroller : a Cortex M3 ( STM32L151VC) low-power 32-bit microcontroller capable to run up to 32MHz

• Radio Interface: Bluetooth Low Energy has been chosen due to compatibility with smart-phones and low power consumption. The chip used is TI CC2541

98 Chapter 6. Activity aware power management for wearable sensors

• Sensors: Invensense MPU 9150 is an IMU, which embeds in a single chip triaxial ac-celerometer, magnetometer and gyroscope. The power consumption of the chip is affected by sampling rate and active sensors.

• NAND Flash Memory: a Micron 8Gbit memory stores sensor data. The size of the memory would allow to save up to 5 days of data sampled at 100Hz

The power management policies adopted for the different activities are shown in table 6.4

Table 6.4: Energy policy for each user activity Activity Sampling Active Radio Bicycling 50 Acc + Gyr Off (Log)

Sit 50 Acc Off (Log)

Lie 30 Acc Off (Log)

In Fig. 6.6 we report the power consumption of the platform without optimization (dashed lines) and the percentage of microcontroller clock cycles utilization by the PM. It is possible to notice that our optimization reduced MCU occupancy required by PM task, optimisation can be quantified in 70% MCU clock cycles reduction for single subject and 50% reduction for multiple subjects scenario. The energy consumption of the system in multiple subjects scenario is in average 12% higher, mainly due to misclassification errors (i.e if activity ”lie” is identified as ”walk” a penalty equal to the difference of power consumption of the two state is given).

Measurements have been performed using the ”single subject” scenario, due to the difficulty to recruit a large number of subjects. Results of Figure 6.6 completely agree with simulation results of section 6.7.2 and shows the efficacy of the proposed PM optimization.

6.9 Conclusion

In this work we have used CAPM concept to build a Power Manager capable to reduce energy consumption of BAN sensor nodes used for subject continuous monitoring. In our work we

com-6.9. Conclusion 99

Run Walk Lie Sit Stair Up Stair Down

0.0%

% of MCU clock cycles used by PM Power (mW)

Figure 6.6: Representation of test platform power consumption and percentage of MCU occu-pancy by the PM with and without optimization. Classifiers has been trained for (*) single subject and (**) multiple subjects scenario

pared different activity classification approaches, among them the use of Decision Tree seems the most suitable; it has same accuracy as other classifiers, but requires less computational ef-fort. We then moved one step further introducing a selective pruning algorithm that is capable to reduce the number of computed features or simplify the tree structure. These improvements resulted in a lightweight power manager that does not impact significantly on platform compu-tational capabilities but increase battery life thanks to its policies. Measurements on a typical platform show that it is possible to achieve energy savings up to 70%.

Chapter 7

Networking operations for activity aware sensor nodes

7.1 Overview

Together with wearables and smartphones, Wireless Body Area Networks (WBANs) are demon-strating to be useful in improving health-related habits and reaching fitness goals. New devices like activity trackers have appeared in myriad, bundled with appealing apps motivating people to care for their health. In motor rehabilitation, information from different body segments is often required, thus WBANs can play an important role, since each single node can be placed in the point of interest of the body [CFM⁺13]. Together with all wearables, WBANs share the need to prolong their lifetime as long as possible, to enhance usability, maintenance and mobility, while keeping the form factor reduced. WBANs have unique challenges in energy efficiency because, differently from wireless sensor networks (WSN), they often have to provide continuous data streaming. In particular, in rehabilitation kind of applications, the WBAN is also asked for real time processing and feedback provisioning.Furthermore, the application imposes strict requirements in quality of service, i.e. accuracy of the measurements, timing, avoidance of data losses, etc.

Power management for WBAN has been proposed in several ways and shares challenges typical 100

7.1. Overview 101

of networked embedded system (i.e. WSN). Software/firmware approaches focus on nodelevel power management such as dynamic voltage and frequency scaling, power-aware scheduling, dynamic power management by using low-power modes, etc. Alternative approaches are based on hardware choices such as use of low-power radios or custom ultra-low power hardware com-ponents or subsystems [UK12]. The best benefits can be reached by combining hardware and software techniques and this is the approach followed in the present paper.

To address power management challenges in WBAN, the development of activity aware power management approaches that can be generalized to other contextual information (from which the name CAPM context aware power management). Depending on user activities andappli-cation goals, the device switches between different power management policies corresponding to different setup of the unit components (i.e. sensor sampling rate, microcontroller configu-ration or transceiver power states, etc.). In another work [CFB14], CAPM is tested at node level. However, the knowledge of the context can influence more than one node in a WBAN and in particular, in this work, we focus on the possibilities offered in a multi-node scenario, augmenting our previous work. In this work a software strategy based on CAPM is combined with the availability of an ultra-low power hardware component, the radio trigger [KKM⁺14]

enabling to selectively wake-up nodes at the best convenience. The use of the wake up radio in WBAN has been proposed in previous works [MP11], but the combination with an activity aware power manager in a rehabilitation scenario has not yet been attempted.

In this chapter we therefore analyze the benefits of using the CAPM approach augmented with a wake-up radio in a gait rehabilitation scenario, where the user wears a WBAN of 3 inertial nodes. Two nodes on the feet are controlled by a third node on the trunk via the wake-up radio trigger, which is activated by the CAPM. This method can benefit from the fact that in the selected rehabilitation scenario nodes can be activated only when the user is walking for periods longer than one minute and remain in a low power state for the rest of the time. We compared the power consumption in absence of power management with the use of the CAPM alone and with the combination of CAPM and radio trigger. We demonstrate that we can augment the lifetime of the WBAN up to 20 times for a typical usage scenario.

102 Chapter 7. Networking operations for activity aware sensor nodes