A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients
Abstract
:1. Introduction
2. Currently Existing Inertial Systems
2.1. Dataloggers
2.2. Open Source Dataloggers
2.3. Parkinson’s Disease Monitoring
3. Requirements of the System
3.1. Clinical Protocol
- Stage 0: In this stage, a baseline exploration is performed by clinicians. They will ask patients to come into doctor’s office to receive an information sheet and the consent form. Then a series of tests are performed and questionnaires are filled in with the aim of mapping the severity of the disease symptoms by means of the UPDRS [43] and Hoehn & Yahr scale [44]. A total of 25 patients will be recruited in two parts (12 patients the first part, 13 patients the second part) for the data collection after providing informed consent according to the Declaration of Helsinki.
- Stage 1: This stage’s main goal is devoted to acquiring inertial data during patients’ activities of daily living in order to adapt two personalized machine learning classifiers to detect bradykinetic gait and freezing of gait. Thus, in this stage patients receive the inertial system and wear it during 3 days at waking hours, at least 10 h, in which the system acquires inertial data at 50 Hz. The fourth day patients go to doctor’s office, where he/she executes, with the sensor in the waist, a series of tests also video-recorded in order to have a gold-standard. The aim of this experiment is to perform a laboratory test of the required real-time algorithms from the inertial system, so that bradykinetic gait and FoG real-time detections are compared to the video recordings. Experiments and results of this test are shown in Section 6 and Section 7, respectively.
- Stage 2: The main objective in this stage is to personalize the RAS system according to patients’ preference and test the system under laboratory conditions. One of the main advantages of the MASPARK system is the capability of providing an actuation once a FoG episode or a bradykinetic gait state is detected. This actuation relies on RAS which enhances gait whenever these symptoms appear [45,46]. Hence, the user is invited to test the headset in laboratory conditions. The headsets will produce different RAS and clinicians will evaluate which one is more effective.
- Stage 3: This stage is dedicated to test the effectivity of the RAS cues in the ADL of patients administered through the MASPARK system (Inertial sensor, smartphone, and headset). Patients will use the 9 × 3 device in two periods of 4 days each, a 4-day period with the actuating system enabled and the other period being the system disabled. Different measures, such as the number of FoG episodes, will be compared among both periods to determine the effectivity of the approach.
- Stage 4: In this stage, patients will use the system during 30 days continuously. At night, patients will charge the 9 × 3 with a standard mobile phone charger (micro-B USB connector). The system will send RAS to the headsets when it detects a FoG or bradykinesia episode, as in Stage 3. The main goal is to test the system in long periods, check usability and analyse patient’s quality of life before and after the use of the system over a longer period.
3.2. Hardware Requirements
- Accelerometer 1 (axes X, Y and Z): 12 bytes.
- Accelerometer 2 (axes X, Y and Z): 12 bytes.
- Accelerometer 3 (axes X, Y and Z): 12 bytes.
- Gyroscope (axes X, Y and Z): 12 bytes.
- Magnetometer (axes X, Y and Z): 12 bytes.
- Barometer: 4 bytes
- Device temperature (accelerometers 1, 2, 3, and barometer): 16 bytes
- Battery status: 4 bytes
- Real-time registers (year, month, day, hour, minute, and second): 6 bytes
- Sample counters: 4 bytes
4. 9 × 3 Hardware Architecture
4.1. Microcontroller
4.2. Sensors
4.2.1. LSM9DS0 Description
4.2.2. LIS2DH Description
4.2.3. Barometric System Description
4.3. Communication and Storage Units
4.4. User Interface
5. Firmware
5.1. Peripheral Microcontroller Management
5.2. Embedded Algorithms
6. Experiments
6.1. Hardware Experiments
- Test 1: Consumption evaluation
- Test 2: Autonomy evaluation (Algorithms)
- Test 3: Autonomy evaluation (Data capture)
- Test 4: Timing evaluation
- Sending a message through Bluetooth every 15 min to ensure Bluetooth communication between the smartphone and the 9 × 3 device.
- Storing algorithmic information within the microSD every minute.
- Storing date, time and battery level every minute.
- Computing algorithms at each window of data (FoG and bradykinetic gait). It requires windowing, filtering, feature extraction and SVM classification in real-time.
- Sampling data frequency set to 40 Hz.
- Receive date and time at the beginning from another device via Bluetooth.
- Store inertial data and other parameters (3 accelerometers, gyroscope, magnetometer, barometer, temperature, sample counter, battery level, date and time) within the microSD at 50 Hz.
6.2. Experiments in the Clinical Environment
7. Results
7.1. Power Consumption and Autonomy Tests
7.2. Timing Analysis of the Algorithms
7.3. Algorithm Evaluation
8. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Name | Manufacturer | Sample Freq.* (Hz) | Autonomy Info.* | Size (mm3) | Weight (g) | Storage Unit | Wireless | Acc * | Gyr * | Mag * | Barometric Pressure | GPS |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Shimmer 3 [19] | Shimmer | 50 | 450 mAh | 51 × 34 × 14 | 23.6 | Yes | Yes | Yes | Yes | Yes | Yes | No |
Physilog 4 Gold [20] | Gaitup (EPFL) | 500 | 21 h | 50 × 37× 9.2 | 19 | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Physilog 4 Silver [20] | Gaitup (EPFL) | 500 | 21 h | 50 × 37 × 9.2 | 19 | Yes | Yes | Yes | Yes | Yes | Yes | No |
3-space™ Sensor Datalogger [21] | Yost Labs | 475 | 5 h | 35 × 60 × 15 | 28 | Yes | Yes | Yes | Yes | Yes | No | No |
MTw Awinda [22] | Xsens | 1000 | 6 h | 47 × 30 × 13 | 16 | No | Yes | Yes | Yes | Yes | No | No |
MTi-G-710 GNSS [23] | Xsens | 375 | 675–950 mW | 57 × 42 × 23.5 | 55 | No | No | Yes | Yes | Yes | Yes | No |
KineO [24] | Technoconcept | 100 | 4 h | 49 × 38 × 19 | 25 | Yes | No | Yes | Yes | Yes | No | No |
Wimu [25] | Realtrack Systems | 1000 | 360 mAh | 85 × 48 × 15 | 60 | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
3DM-GX4 [26] | MicroStrain | 1000 | 100 mA | 36 × 24.4 × 11.1 | 16.5 | No | No | Yes | Yes | No | Yes | No |
Dynaport MM [27] | McRoberts | 200 | 14 days | 106.6 × 58 × 11.5 | 55 | No | Yes | Yes | Yes | Yes | Yes | No |
BioRadio [28] | GLNeuroTech | 250 | 8 h | 100 × 60 × 20 | 113.4 | Yes | No | Yes | Yes | No | No | No |
Research Tracker 6 [29] | Stayhealthy | 20 | 25 h | 51 × 51 × 13 | 51 | Yes | No | Yes | Yes | No | No | No |
activPAL3 [30] | paltechnologies | 10 | 10 days | 53 × 35 × 7 | 15 | No | No | Yes | No | No | No | No |
x-IMU [31] | x-IO Technologies | 512 | 150 mA | 57 × 38 × 21 | 49 | Yes | Yes | Yes | Yes | Yes | No | No |
STT-IWS [32] | STT-Systems | 400 | 3.5 h | 56 × 38.5 × 18 | 46 | Yes | Yes | Yes | Yes | Yes | Yes | No |
9 × 2 (2013) [17] | UPC-CETpD | 200 | 36.8 h | 99 × 53 × 19 | 78 | Yes | Yes | Yes | Yes | Yes | No | No |
Stage 0 | Stage 1 | Stage 2 | Stage 3 | Stage 4 | |||
---|---|---|---|---|---|---|---|
Baseline exploration | Data capture at patients home | Laboratory validation | RAS personalization | Use system at home without RAS | Washout period | Use system at home with RAS | Use system at home with RAS |
Patient’s visit | 3 days | Patient’s visit | Patient’s visit | 4 days | 30 days | 4 days | 30 days |
Microcontroller Features | dsPIC33FJ128MC804 (9 × 2) | STM32F415RG (9 × 3) |
---|---|---|
Maximum Speed (Hz) | 80 | 168 |
Flash Memory (kB) | 128 | 1024 |
RAM memory (kB) | 16 | 192 + 4 (DMA) |
DMA streams | 8 | 16 |
Consumption at full work (mA) * | 65 | 43 |
Consumption in Idle mode (mA) * | 34 | 9 |
Consumption in Sleep mode (mA) * | 0.01 | 0.004 |
SDIO | No | Yes |
I2C Bus | 2 | 3 |
Computing method | Fixed point | Floating point |
Computing performance | 40 MIPS | 210DMIPS (Dhrystone 2.1) |
Parameters | BMP280 | LPS25H | MS5637 |
---|---|---|---|
Range (mbar) | 300–1100 | 260–1260 | 10–2000 |
Relative accuracy (mbar) | 0.12 | 0.1 | 0.1 |
Absolute accuracy (mbar) | 1 | 1 | 4 |
Resolution RMS (mbar) | 0.0016 | 0.000244 | 0.016 |
Pressure Noise (mbar) | 0.0013 | 0.01 | 0.5 |
Compensation | External | Internal | External |
Size (mm3) | 2 × 2.5 × 0.95 | 2.5 × 2.5 × 1 | 3 × 3 × 0.9 |
Consumption @1 Hz (μA) | 2.7 | 25 | 20.1 |
Maximum Data Rate (Hz) | 26.7 | 25 | 60 |
Oversampling | 16 | 512 | 8192 |
Algorithm Block | Algorithm | Temporal Level * |
---|---|---|
2nd order filters [3,41,60,61,62,63,64] | All | Sample calculation |
Mean 3 accelerometer axes [3,41,60,63,64] | Brady, FoG, Gait | Window output |
Standard deviation [3,41,60,63,64] | Brady, FoG, Gait | Window output |
STFT—Band 1 [3,41,61,62,63,64] | Gait, Dysk, Brady | Window output |
STFT—Band 2 [3,41,61,62,63,64] | Gait, Dysk, Brady | Window output |
STFT—dyskinetic band [61] | Dyskinesia | Window output |
STFT—non-continuous movement band [61] | Dyskinesia | Window output |
STFT—Postural Transition band [60,61,63] | Dyskinesia, FoG | Window output |
SVM Walk [3,41,61,62,64] | Brady, Dysk, Gait | Window output |
Dyskinesia tree-based classifier [61] | Dyskinesia | Window output |
Step detector [3,41,62,64] | Brady, Gait | Window output |
Stride detector [3,41,62,64] | Brady, Gait | Window output |
Cadence Estimation [62] | Gait | Window output |
Step length [62] | Gait | Window output |
Step velocity [62] | Gait | Window output |
Fluidity computation [3,41,62,64] | Bradykinesia | Window output |
SVM—FoG yes-no [60,63] | FoG | Window output |
Decision tree based classifier for strides [3,41,62,64] | Bradykinesia | Minute output |
Dyskinesia 1 min [3,41] | Dyskinesia | Minute output |
Bradykinesia 1 min [3,41,64] | Bradykinesia | Minute output |
Cadence Estimation 1 min [3,41] | Gait | Minute output |
Step length 1 min [3,41] | Gait | Minute output |
Step velocity 1 min [3,41] | Gait | Minute output |
Tree-based classifier for ON/OFF state [3,41] | ON/OFF | 10-min output |
Patients | Gender | H&Y (ON) | H&Y (OFF) | Age | UPDRS III (OFF) | UPDRS III (ON) |
---|---|---|---|---|---|---|
Patient 1 | Male | 2.5 | 3 | 62 | 5 | 10 |
Patient 2 | Male | 2.5 | 3 | 69 | 18 | 27 |
Patient 3 | Male | 2 | 3 | 70 | 7 | 24 |
Patient 4 | Male | 2.5 | 3 | 54 | 21 | 35 |
Patient 5 | Male | 2.5 | 3 | 61 | 8 | 40 |
Patient 6 | Female | 2 | 3 | 59 | 11 | 20 |
Patient 7 | Male | 2.5 | 3 | 76 | 42 | 45 |
Patient 8 | Female | 2.5 | 3 | 71 | 11 | 24 |
Patient 9 | Female | 2.5 | 3 | 66 | 4 | 12 |
Patient 10 | Male | 2.5 | 3 | 66 | 24 | 35 |
Patient 11 | Male | 2 | 2.5 | 61 | 17 | 32 |
Patient 12 | Female | 2.5 | 3 | 71 | 6 | 17 |
Name | 9 × 3 |
Manufacturer | UPC-CETpD |
Sample Frequency (Hz) | 1 to 1600 |
Autonomy when sampling at 50 Hz | 23.09 days at waking hours (Algorithms) |
9.6 days continuously (Algorithms) | |
3.81 days continuously (Data Capture) | |
9.14 days at waking hours (Data Capture) | |
Size (mm3) | 99 × 53 × 19 |
Weight (g) | 83 |
Storage Unit | Yes |
Wireless | Yes |
Accelerometer | Yes |
Gyroscope | Yes |
Magnetometer | Yes |
Barometric Pressure | Yes |
GPS | No |
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Rodríguez-Martín, D.; Pérez-López, C.; Samà, A.; Català, A.; Moreno Arostegui, J.M.; Cabestany, J.; Mestre, B.; Alcaine, S.; Prats, A.; Cruz Crespo, M.D.l.; et al. A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients. Sensors 2017, 17, 827. https://doi.org/10.3390/s17040827
Rodríguez-Martín D, Pérez-López C, Samà A, Català A, Moreno Arostegui JM, Cabestany J, Mestre B, Alcaine S, Prats A, Cruz Crespo MDl, et al. A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients. Sensors. 2017; 17(4):827. https://doi.org/10.3390/s17040827
Chicago/Turabian StyleRodríguez-Martín, Daniel, Carlos Pérez-López, Albert Samà, Andreu Català, Joan Manuel Moreno Arostegui, Joan Cabestany, Berta Mestre, Sheila Alcaine, Anna Prats, María De la Cruz Crespo, and et al. 2017. "A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients" Sensors 17, no. 4: 827. https://doi.org/10.3390/s17040827
APA StyleRodríguez-Martín, D., Pérez-López, C., Samà, A., Català, A., Moreno Arostegui, J. M., Cabestany, J., Mestre, B., Alcaine, S., Prats, A., Cruz Crespo, M. D. l., & Bayés, À. (2017). A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients. Sensors, 17(4), 827. https://doi.org/10.3390/s17040827