Real-time data acquisition and analysis for predictive modelling of mental healthcare in Indian women with menstrual disorders: unveiling insights and implications from extensive surveys

Figures & data

Table 1. Prior literature works in the field of mental healthcare.

Ref	Methodology	Problems & Drawbacks
[20]	Deep learning methodologies for mental health diagnosis	Due to increased data dimensionality, it consumes more time to process the data.
[21]	Text mining approach for analyzing the effects of mental health	Limited analysis, and ineffective processing.
[22]	Machine learning model	Lower prediction rate and performance outcomes.
[23]	Random survival forest classifier	Complexity in prediction and lack of efficacy.
[24]	Optimized adaptive CNN model	High error rate and low prediction accuracy.
[25]	Embedded LSTM model	Consumes increased time for disorder prediction and complex training & testing.

Figure 1. Overview of the proposed work.

Figure 2. Extensive dataset generation by real-time monitoring with near real-time experience.

Table 2. Physiological parameters.

	Parameters	Embedded devices
1.	Basal body temperature	Basal body thermometer
2.	Sleep sensing	Biochemical sensor: Sleep (fabricated)
3.	Sweat monitoring	Biochemical sensor: Sweat (fabricated)
4.	Whole body thermo flow	Heat flux sensor
5.	Sleep posture Physical activity	3-axis accelerometer sensors
6.	Stress sensing from saliva Galvanic skin response	Electrodermal activity sensor
7.	To sense blood oxygen level	Pulse oximeter

Table 3. Metabolic parameters.

	Parameters	Embedded devices
1.	Stress level (Threshold fixation) Computation at edge-node (smart phone)	Electrodermal sensor actuates stress level monitor
2.	Total energy expenditure Active energy expenditure	Calorie burnt calculation from 3-axis accelerometer
3.	Sleep efficiency	3-axis accelerometer

Table 4. Psychological parameters.

	Parameters	Embedded devices
1.	Eating style (frequency of swallowing)	3-axis accelerometer (Pre-requisite: Subject should wear the watch in her dominant hand)
2.	Sleeping pattern	Pattern matched embedded in the edge device
3.	Biphasic body temperature	Differential computation from readings of basal body thermometer and heat flux

Table 5. Biochemical parameters.

	Parameters	Embedded devices
1.	Fine lines in the skin	Skin tensiometric sensor
2.	Prolactin mensuration	Ionophore-based optical sensor

Figure 3. Mathematical modelling of correlation between menstrual disorder and mental health (Blockchain based Transfer learning).

Figure 4. Focusing and validating on novel data-driven modelling with transfer learning.

Table 6. Questionnaire to inquire about the psychological state of the girls/women during the menstrual timings.

Q.no	Question	Option1	Option 2	Option 3	Option 4
GENERAL: Individual person’s lifestyle questions
1.	Your current age
2.	Your Marital status	Married	UnMarried
3.	What is your period cycle?	24 - 28 days	28 - 34 days	34 - 40 days	> 40 days
4.	How old were you when you started menstruating
5.	What type of hygiene do you prefer during the menstrual timing?	Disposable sanitary pads	Reusable cloth pads	Menstrual cups	Others
6.	What are the abnormalities you face during your periods?	Unbearable cramps	Bearable cramps	Body pain	Smooth periods
Physiological, Metabolic and psychological parameters
7.	Sleeping pattern during periods timing	Insomnia	Frequent awakening	Sleep has not been sufficiently refreshing	Normal
8.	Do you feel day time disturbances associated with menstruation?	Fatigue	Sleepiness	Decreased alertness	Anxiety
9.	Do you feel any slight rise in the body temperature in this period?	Yes		No
10.	Do you feel excessive sweating before menstruation, especially at night?	Yes		No
11.	Mention your comfortable sleeping position during menstrual cramps.	Sleeping in the fetal position	Sleeping on the back	Sleeping on the side
12.	Can you involve in your routine physical activity during your periods?	Yes		No
13.	What are the oral changes you would encounter during your periods?	Bleeding gums	Red swollen gums	Increased saliva secretion	Normal
14.	What are the negative emotions that affect your mood in periods?	Anger	Irritability	Mood swings	Normal
15.	What is your appetite during Menstrual times?	Hungry	Low appetite	Cravings	Normal
16.	Are you so stressed that your intake painkillers or muscle relaxant to get rid of menstrual cramps?	Yes		No
17.	Do you feel notable rise in temperature in the pelvic region during the menstrual times?	Yes		No
18.	Have irregular periods? If so, do you know the reason?	Stress	Obesity	Thyroid	Don’t know the reason

Figure 5. Age of the subjects under study.

Figure 6. Marital status of the subjects under study.

Figure 7. Menstrual cycle duration for the subjects under study.

Figure 8. Physical abnormalities faced by the subjects during the week of menstruation.

Figure 9. Sleeping pattern or the difficulties in sleeping for the subjects during the week of menstruation.

Figure 10. Body temperature status of the subjects during the menstruation week.

Figure 11. Pelvic region or lower abdomen temperature during the menstruation week.

Figure 12. Eating habits or the pattern of eating by the subjects during the menstruation week.

Figure 13. Status of daily routine physical activities of the subjects during the week of the menstruation.

Figure 14. Mean response time of components with LAN (induced delay of 250 ms).

Figure 15. System throughput with LAN (induced delay of 250 ms).

Figure 16. Mean response time of blockchain with cloud storage (induced delay of 250 ms).

Figure 17. Throughput with blockchain running on AWS cloud (induced delay of 250 ms).

Figure 18. Throughput comparisons between blockchain and LAN in a multiserver environment.

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